MOTIVATION: Spatial transcriptomics (ST) enables molecular profiling within native tissue architecture, yet accurate delineation of spatial domains in ST data is challenging, as it demands the coordinated integration of transcriptomic, spatial, and tissue histological information. RESULTS: We present SRLST, an unsupervised representation learning framework that holistically harmonize these three complementary data modalities to precisely uncover tissue organization. SRLST employs a dual-graph variational autoencoding strategy to jointly model spatial proximity and morphological relations, fusing these with gene-expression embeddings into a unified latent space. Across distinct experimental datasets, SRLST consistently outperforms existing methods in delineating cortical organization, identifying small discontinuous tissue compartments, and capturing complex intratumor heterogeneity. AVAILABILITY AND IMPLEMENTATION: The code implementation of the SRLST algorithm is available at https://github.com/lanbiolab/SRLST.

MicroRNAs (miRNAs) play a vital role in regulating a wide range of biological functions and are key players in the development of many complex human diseases, making them novel therapeutic targets for drug development. Given the high expenses and time demands of traditional experimental methods, it is essential to develop efficient computational approaches for predicting miRNA-drug interactions (MDIs). This article presents a dual-channel learning framework, SSMDI, based on structural features and Signed Bipartite Graph Neural Network (SBGNN) for predicting MDIs. Firstly, Graph Isomorphism Networks (GIN) is employed to extract molecular graph features of drugs. Meanwhile, a combined framework of Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM) network and Self-attention Mechanism is utilized to capture sequence features of miRNAs. Compared with traditional networks, signed networks can deliver richer semantic information in drugs and miRNAs. Therefore, SBGNN is then used to aggregate and update the signed topological features of miRNAs and drugs. Finally, structural and signed topological features are integrated to predict MDIs. The predictive performance of the model is evaluated using 5-fold cross-validation (CV), achieving AUC of 0.9447 and AUPR of 0.9238. The case study further demonstrates the effectiveness of SSMDI in predicting MDIs. In summary, the SSMDI model proves to be an accurate tool for predicting MDIs, which holds significant implications for drug development and miRNA-based therapeutic research.

Autonomous Driving Systems (ADS) are critical components of Intelligent Transportation Systems (ITS), where vehicle-level reliability has a direct bearing on road traffic safety. Evaluating ADS performance in complex environments remains challenging due to the absence of test oracles and the heavy reliance on deep learning. To address these challenges, this study proposes a novel metamorphic testing framework tailored for vision-based ADS. First, causal inference is employed to extract key environmental factors from high-dimensional observational traffic data, thereby reducing the test space. Second, a multi-objective optimization algorithm integrating causal counterfactual reasoning is developed to quantify the challenges associated with specific combinations of causal factors, enabling cost-effective exploration of test conditions. Third, low-risk source images are systematically transformed into hazardous driving scenes through a fine-tuned diffusion model, allowing ADS evaluation to be guided by metamorphic relations (MRs). Empirical experiments show that the proposed method achieves a higher fault detection ratio than the strongest baseline in four out of five ADS models, with relative gains ranging from 18.1% to 88.9%. Data augmentation experiments further demonstrate that incorporating MR-violating test cases can reduce ADS prediction errors by up to 13.67%, with these benefits preserved in real-world road traffic datasets through domain adaptation. This study highlights a new pathway for validating the reliability of vision-based ADS driven by deep learning, thereby supporting the deployment of safer road transportation. The source code for our methods and baselines is available athttps://github.com/SafeDL/AutoMetTest

Autism Spectrum Disorder is a neurodevelopmental disorder that typically manifests in early childhood, characterized by difficulties in social interaction and communication, narrow interests, and repetitive behaviors. Although there is currently no complete cure, early intervention is crucial for alleviating symptoms and developing skills. The diagnosis of autism is often subjective, time-consuming, and costly. This study proposes a DRD model that combines DenseNet121, ResNet152, and decision tree algorithms, applied to facial images aligned using MTCNN to identify autism. The model achieved 94% accuracy and 0.9768 AUC-ROC on the New-Kaggle dataset, and 98 % accuracy with 0.9948 AUC-ROC on the YTUIA dataset, significantly outperforming the results obtained by using DenseNet121 and ResNet152 models alone. Compared to other studies using facial images to identify autism, our method showed higher recognition accuracy. This suggests that using this approach for screening children with autism is feasible and can offer potential for early detection across large populations.

MicroRNAs (miRNAs) are key post-transcriptional regulators closely associated with human diseases. Identifying miRNA-drug associations (MDAs) is important for precision drug discovery, yet experimental validation remains costly and labor-intensive. To address this limitation, we propose MVR-MDA, a multi-modal deep learning framework that integrates heterogeneous biological information for efficient MDA prediction. MVR-MDA combines three complementary feature sources: pretrained 3D molecular representations from Uni-Mol to enhance generalization to novel drugs, intra-attribute features from MACCS fingerprints and miRNA sequences refined by BiGRU, and inter-topological features captured through SDNE from the miRNA-drug interaction graph. These representations are fused to learn both intrinsic molecular properties and global relational patterns. We evaluate MVR-MDA on ncDR and RNAInter through 5-fold cross-validation, achieving superior prediction performance compared to state-of-the-art methods. A case study on 5-Fluorouracil and hsa-miR-146a further validates the biological relevance of our predictions. Overall, MVR-MDA provides an effective computational tool for discovering potential MDAs, supporting novel therapeutic target identification and accelerating drug repositioning.

Fine-tuning large language models (LLMs) faces significant memory challenges due to the high cost of back-propagation.MeZO addresses this issue using zeroth-order (ZO) optimization, matching memory usage to inference but suffering from slow convergence due to varying curvatures across model parameters.To overcome this limitation, we propose HELENE, a scalable and memoryefficient optimizer that integrates annealed A-GNB gradients with diagonal Hessian estimation and layer-wise clipping as a second-order pre-conditioner.HELENE provably accelerates and stabilizes convergence by reducing dependence on total parameter space and scaling with the larger layer dimension.Experiments on RoBERTa-large and OPT-1.3Bdemonstrate superior performances, achieving up to 20× speedup over MeZO with an average accuracy improvement of 1.5%.HELENE also supports full and parameter-efficient fine-tuning methods, outperforming several state-of-the-art optimizers.

Echocardiography (ECHO) is essential for cardiac assessments, but its video quality and interpretation heavily relies on manual expertise, leading to inconsistent results from clinical and portable devices. ECHO video generation offers a solution by improving automated monitoring through synthetic data and generating high-quality videos from routine health data. However, existing models often face high computational costs, slow inference, and rely on complex conditional prompts that require experts' annotations. To address these challenges, we propose ECHOPulse, an ECG-conditioned ECHO video generation model. ECHOPulse introduces two key advancements: (1) it accelerates ECHO video generation by leveraging VQ-VAE tokenization and masked visual token modeling for fast decoding, and (2) it conditions on readily accessible ECG signals, which are highly coherent with ECHO videos, bypassing complex conditional prompts. To the best of our knowledge, this is the first work to use time-series prompts like ECG signals for ECHO video generation. ECHOPulse not only enables controllable synthetic ECHO data generation but also provides updated cardiac function information for disease monitoring and prediction beyond ECG alone. Evaluations on three public and private datasets demonstrate state-of-the-art performance in ECHO video generation across both qualitative and quantitative measures. Additionally, ECHOPulse can be easily generalized to other modality generation tasks, such as cardiac MRI, fMRI, and 3D CT generation. We will make the synthetic ECHO dataset, along with the code and model, publicly available upon acceptance.

Lesion localization using machines greatly assists doctors in diagnosing diseases and providing better treatment for patients, which is significant for intelligent healthcare. Unlike natural images, the background and target objects in medical images are often difficult to distinguish, and obtaining medical annotations for training models is challenging. This makes accurate lesion localization extremely difficult. In this paper, we propose a counter-factual generation prompt learning framework for lesion localization in medical images. First, we employ the class association embedding method for separating lesion-related information from lesion-irrelevant information in medical images. By embedding different lesion-related information, we generate counterfactual samples, and obtain lesion-related knowledge based on comparison. We further process the lesion-related knowledge and obtain a prior prompt, which is then fed into a well-known segmentation network for more accurate and detailed lesion localization. To accurately acquire lesion-related knowledge, we propose an irrelevant feature similarity transfer method to reduce the interference of irrelevant knowledge. Experimental results show that our method achieves excellent lesion localization results without requiring pixel-level annotations for training, and also outperforms other existing localization algorithms.

Deep learning demonstrates effectiveness across a wide range of tasks. However, the dense and over-parameterized nature of these models results in significant resource consumption during deployment. In response to this issue, weight pruning, particularly through$N: M$sparsity matrix multiplication, offers an efficient solution by transforming dense operations into semisparse ones.$N: M$sparsity provides an option for balancing performance and model accuracy, but introduces more complex programming and optimization challenges. To address these issues, we design a systematic top-down performance analysis model for$N: M$sparsity. Meanwhile, NM-SpMM is proposed as an efficient general$N: M$sparsity implementation. Based on our performance analysis, NM-SpMM employs a hierarchical blocking mechanism as a general optimization to enhance data locality, while memory access optimization and pipeline design are introduced as sparsity-aware optimization, allowing it to achieve close-to-theoretical peak performance across different sparsity levels. Experimental results show that NM-SpMM is 2.1x faster than nmSPARSE (the state-of-the-art for general$N: M$sparsity) and 1.4× to 6.3× faster than cuBLAS's dense GEMM operations, closely approaching the theoretical maximum speedup resulting from the reduction in computation due to sparsity. NM-SpMM is open source and publicly available at https://github.com/M-H482/NM-SpMM.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition typically emerging in early childhood, characterized by challenges in social interaction, communication deficits, restricted interests, and repetitive behavioral patterns. While no complete cure exists, early intervention remains critical for symptom management and skill development in affected children. Traditional diagnostic approaches depend on clinical assessments by mental health experts following established criteria, yet these methods are constrained by subjectivity, prolonged evaluation periods, and high costs. Aiming at the problem that most of the existing studies use a single deep learning model to classify autistic facial images with insufficient accuracy, this study presents an innovative deep learning framework MFAN that combines pre-trained VGG16 and MobileNetV2 architectures with a Convolutional Block Attention Module (CBAM). By exploiting the discriminative facial features distinguishing autistic and neurotypical individuals, the proposed model aims to classify ASD status using facial images. Evaluation metrics include accuracy, precision, and recall. After rigorous training and validation protocols, the MFAN model achieved 92.67% test accuracy and an AUC-ROC of 0.9635. These results outperform standalone VGG16 and MobileNetV2 models and their simple combinations, demonstrating enhanced classification efficacy. The findings highlight the potential of deep transfer learning for scalable ASD screening, offering a promising tool for early detection in population-level contexts.

Polypharmacy is a common means of clinical treatments, but detecting drug-drug interactions (DDIs) behind unexpected effects can be costly and faces clinical limitations. Recently, graph neural networks (GNNs) have demonstrated encouraging performance in predicting DDIs. However, most studies overlook the comprehensive aspects of DDIs, such as the coexistence of types of pharmacological changes and the asymmetric roles of drugs. In this article, we define new prediction tasks, taking into account both enhancive or depressive changes and the roles of drugs, and then establish spectral GNNs to predict comprehensive information of DDIs. First, we formally define several tasks, including joint prediction tasks designed to leverage both types and directions. These tasks deduce to sub-tasks in previous studies. Then, we propose a unified framework, the MKMGCN-DDI, via introducing two Magnetic Laplacian matrices to encode comprehension information within DDIs, defining multiple graph filters, and designing multiple-kernel based Magnetic graph convolutional networks (MKMGCN). Experiments across three datasets show that it not only has good adaptability to multiple tasks but also significantly improves results on simple tasks. Case studies on breast neoplasms and lung neoplasms verify its feasibility, as over half of top-10 items are supported.

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology.

Significant progress in medical research in recent years has led to the successful treatment of numerous illnesses. Unfortunately, effective treatments for several neurological disorders-particularly those affecting motor neurons-remain elusive, not only significantly impairing patients' quality of life but also placing a substantial financial burden on both individuals and society. To address this gap, scientists are focusing on model organisms to unravel pathogenic mechanisms and develop treatment strategies. Among various model organisms, Caenorhabditis elegans (C. elegans) has shown special relevance in neurological disorder research. Escape responses, including reversals and omega turns, are controlled by motor neurons. Thus, the escape response in C. elegans provides researchers with a reliable observational metric to evaluate the functional state of motor neurons. We developed a head and tail position localization model to determine the positions of the nematode's head and tail, enabling automated counting of escape responses. We further clarified the association between escape response and motor neuron function by comparing the escape response of normal nematodes to those with motor neuron deficiencies. Consequently, the automated counting of C. elegans escape responses is a useful tool for assessing the health of motor neurons. It may offer strong backing for the exploration of neurodegenerative disease therapeutics.

Salient and small lesions (e.g., microaneurysms on fundus) both play significant roles in real-world disease diagnosis under medical image examinations. Although deep neural networks (DNNs) have achieved promising medical image classification performance, they often have limitations in capturing both salient and small lesion information, restricting performance improvement in imbalanced medical image classification. Recently, with the advent of DNN-based style transfer in medical image generation, the roles of clinical styles have attracted great interest, as they are crucial indicators of lesions. Motivated by this observation, we propose a novel Adaptive Dual-Axis Style-based Recalibration (ADSR) module, leveraging the potential of clinical styles to guide DNNs in effectively learning salient and small lesion information from a dual-axis perspective. ADSR first emphasizes salient lesion information via global style-based adaptation, then captures small lesion information with pixel-wise style-based fusion. We construct an ADSR-Net for imbalanced medical image classification by stacking multiple ADSR modules. Additionally, DNNs typically adopt cross-entropy loss for parameter optimization, which ignores the impacts of class-wise predicted probability distributions. To address this, we introduce a new Class-wise Statistics Loss (CWS) combined with CE to further boost imbalanced medical image classification results. Extensive experiments on five imbalanced medical image datasets demonstrate not only the superiority of ADSR-Net and CWS over state-of-the-art (SOTA) methods but also their improved confidence calibration results. For example, ADSR-Net with the proposed loss significantly outperforms CABNet50 by 21.39% and 27.82% in F1 and B-ACC while reducing 3.31% and 4.57% in ECE and BS on ISIC2018.

With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However, biomedical researchers currently have limited knowledge about LLM. Therefore, there is an urgent need for a summary of LLM applications in biomedical data analysis. Herein, we propose this review by summarizing the latest research work on LLM in biomedicine. In this review, LLM techniques are first outlined. We then discuss biomedical datasets and frameworks for biomedical data analysis, followed by a detailed analysis of LLM applications in genomics, proteomics, transcriptomics, radiomics, single-cell analysis, medical texts and drug discovery. Finally, the challenges of LLM in biomedical data analysis are discussed. In summary, this review is intended for researchers interested in LLM technology and aims to help them understand and apply LLM in biomedical data analysis research.

Functional Magnetic Resonance Imaging (fMRI) is used for extracting blood oxygen signals from brain regions to map brain functional connectivity for brain disease prediction. Despite its effectiveness, fMRI has not been widely used: on the one hand, collecting and labeling the data is time-consuming and costly, which limits the amount of valid data collected at a single healthcare site; on the other hand, integrating data from multiple sites is challenging due to data privacy restrictions. To address these issues, we propose a novel, integrated Federated learning and Split learning Spatio-temporal Graph framework (F G). Specifically, we introduce federated learning and split learning techniques to split a spatio-temporal model into a client temporal model and a server spatial model. In the client temporal model, we propose a time-aware mechanism to focus on changes in brain functional states and use an InceptionTime model to extract information about changes in the brain states of each subject. In the server spatial model, we propose a united graph convolutional network to integrate multiple graph convolutional networks. Integrating federated learning and split learning, F G can utilize multi-site fMRI data without violating data privacy protection and reduce the risk of overfitting as it is capable of learning from limited training data sets. Moreover, it boosts the extraction of spatio-temporal features of fMRI using spatio-temporal graph networks. Experiments on ABIDE and ADHD200 datasets demonstrate that our proposed method outperforms state-of-the-art methods. In addition, we explore biomarkers associated with brain disease prediction using community discovery algorithms using intermediate results of F G. The source code is available at https://github.com/yutian0315/FS2G.

Learning with little data is challenging but often inevitable in various application scenarios where the labeled data are limited and costly. Recently, few-shot learning (FSL) gained increasing attention because of its generalizability of prior knowledge to new tasks that contain only a few samples. However, for data-intensive models such as vision transformer (ViT), current fine-tuning-based FSL approaches are inefficient in knowledge generalization and, thus, degenerate the downstream task performances. In this article, we propose a novel mask-guided ViT (MG-ViT) to achieve an effective and efficient FSL on the ViT model. The key idea is to apply a mask on image patches to screen out the task-irrelevant ones and to guide the ViT focusing on task-relevant and discriminative patches during FSL. Particularly, MG-ViT only introduces an additional mask operation and a residual connection, enabling the inheritance of parameters from pretrained ViT without any other cost. To optimally select representative few-shot samples, we also include an active learning-based sample selection method to further improve the generalizability of MG-ViT-based FSL. We evaluate the proposed MG-ViT on classification, object detection, and segmentation tasks using gradient-weighted class activation mapping (Grad-CAM) to generate masks. The experimental results show that the MG-ViT model significantly improves the performance and efficiency compared with general fine-tuning-based ViT and ResNet models, providing novel insights and a concrete approach toward generalizing data-intensive and large-scale deep learning models for FSL.

Large language models (LLMs) such as ChatGPT have recently demonstrated significant potential in mathematical abilities, providing a valuable reasoning paradigm consistent with human natural language. However, LLMs currently have difficulty in bridging perception, language understanding, and reasoning (PLR) capabilities due to incompatibility of the underlying information flow among them, making their reasoning ability not fully elicited and challenging to accomplish complicated reasoning tasks autonomously. To resolve the above problem, a novel method called ChatABL is proposed by integrating LLMs into an abductive learning (ABL) framework, capable of unifying the three abilities effectively in a more user-friendly and understandable manner. Initially, the proposed method uses LLMs to correct the incomplete logical facts for optimizing the perception module, by summarizing and reorganizing domain knowledge represented in natural language format. Then, the perception module also provides necessary logical reasoning materials for feeding LLMs. Finally, these parts are integrated into a dynamic closed-loop system by introducing the feedback form and automatic learning strategies to mutually promote their performance. As a testbed, the variable-length handwritten equation decipherment (HED), an abstract expression of the Mayan calendar decoding, is used to demonstrate that ChatABL has reasoning ability beyond most existing state-of-the-art methods, which has been well-supported by comparative studies. To the best of authors' knowledge, the proposed ChatABL is the first attempt to explore a possible and novel avenue to approaching human-level cognitive ability via natural language interaction by means of ChatGPT.

Increasing multiple behavior recommendation models have achieved great successes. However, many models do not consider commonalities and differences between behaviors and data sparsity of the target behavior. This article proposes a novel multi-behavior recommendation model based on contrastive clustering learning (MBRCC). Specifically, the graph convolutional network (GCN) is employed to obtain the embeddings of users and items, respectively. Then, three kinds of tasks (including behavior-level embedding, instance-level embedding, and cluster-level embedding) are designed to optimize the embeddings of users and items. In behavior-level embedding, we design an adaptive parameter learning strategy to analyze the impact of auxiliary behaviors on the target behavior. Then, the embeddings of users for each behavior are weighted to obtain the final embeddings of users. In instance-level embedding, we employ contrastive learning to analyze the instances of user and item for mitigating the issue of data sparsity. In cluster-level embedding, we design a new cluster contrastive learning method to capture the similarity between groups of user and item. Finally, we combine these three tasks to improve the quality of the embeddings of users and items. We conduct extensive experiments on three real-world datasets and experimental results indicate that the MBRCC remarkably outperforms numerous existing recommendation models.

Image classification is a primary task in data analy-sis where explainable models are crucially demanded in various applications. Although amounts of methods have been proposed to obtain explainable knowledge from the black-box classifiers, these approaches lack the efficiency of extracting global knowl-edge regarding the classification task, thus is vulnerable to local traps and often leads to poor accuracy. In this study, we propose a generative explanation model that combines the advantages of global and local knowledge for explaining image classifiers. We develop a representation learning method called class association embedding (CAE), which encodes each sample into a pair of separated class-associated and individual codes. Recombining the individual code of a given sample with altered class-associated code leads to a synthetic real-looking sample with preserved individual characters but modified class-associated features and possibly flipped class assignments. A building-block coherency feature extraction algorithm is proposed that efficiently separates class-associated features from individual ones. The extracted feature space forms a low-dimensional manifold that visualizes the classification decision patterns. Explanation on each individual sample can be then achieved in a counter-factual generation manner which continuously modifies the sample in one direction, by shifting its class-associated code along a guided path, until its classification outcome is changed. We compare our method with state-of-the-art ones on explaining image classification tasks in the form of saliency maps, demonstrating that our method achieves higher accuracies. The class-associated manifold not only helps with skipping local traps and achieving accurate explanation, but also provides insights to the data distribution patterns that potentially aids knowledge discovery. The code is available at https://github.com/xrtll/xAI-CODE.

Machine-based brain tumor segmentation can help doctors make better diagnoses. However, the complex structure of brain tumors and expensive pixel-level annotations present challenges for automatic tumor segmentation. In this paper, we propose a counterfactual generation framework that not only achieves exceptional brain tumor segmentation performance without the need for pixel-level annotations, but also provides explainability. Our framework effectively separates class-related features from class-unrelated features of the samples, and generate new samples that preserve identity features while altering class attributes by embedding different class-related features. We perform topological data analysis on the extracted class-related features and obtain a globally explainable manifold, and for each abnormal sample to be segmented, a meaningful normal sample could be effectively generated with the guidance of the rule-based paths designed within the manifold for comparison for identifying the tumor regions. We evaluate our proposed method on two datasets, which demonstrates superior performance of brain tumor segmentation. The code is available at https://github.com/xrt11/tumor-segmentation.

Test the safety of Autonomous Driving Systems (ADS) with realistic traffic conditions is important to the insurance industry, legislators, and third-party technical services. Approaches for ADS testing can be divided into two main categories: physical test and virtual test, as shown in Fig. 1.

Visual-based object detection has become a crucial component in the realm of autonomous vehicles. However, conducting reliable testing for such systems remains unresolved. In this paper, we advocate for the application of causal inference to investigate the pivotal environmental factors influencing detection accuracy. Through the integration of diffusion models, we address the specialized conditional generalization of hazardous testing images. Our approach involves the construction of observational data to attribute key factors and fine-tune the diffusion model. Additionally, we introduce an optimal prompt words search method that strikes a balance between test coverage and level of challenge. Subsequently, leveraging these optimal prompts, we propose a cost-effective testing image generation through both "Text2Scene" and "Image2Scene" fashions. The experimental results indicate that, on the generalized dataset, the performance of object detection algorithms is the poorest, with the average detection accuracy decreasing from 0.81 to 0.285. Moreover, retraining object detection models on our generalized critical test cases can ultimately enhance algorithm performance, achieving a median accuracy improvement of up to 8.13%. Overall, our research proposes a novel approach to generalize test cases, thereby contributing to the advancement and deployment of safer autonomous vehicles.

Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarkers and identify pathogenic targets. However, current methods ignore the potential correlations between samples in integrating multi-omics data. In addition, providing accurate biological explanations still poses significant challenges due to the complexity of deep learning models. Therefore, there is an urgent need for a deep learning-based multi-omics integration method to explore the potential correlations between samples and provide model interpretability. Herein, we propose a novel interpretable multi-omics data integration method (DeepKEGG) for cancer recurrence prediction and biomarker discovery. In DeepKEGG, a biological hierarchical module is designed for local connections of neuron nodes and model interpretability based on the biological relationship between genes/miRNAs and pathways. In addition, a pathway self-attention module is constructed to explore the correlation between different samples and generate the potential pathway feature representation for enhancing the prediction performance of the model. Lastly, an attribution-based feature importance calculation method is utilized to discover biomarkers related to cancer recurrence and provide a biological interpretation of the model. Experimental results demonstrate that DeepKEGG outperforms other state-of-the-art methods in 5-fold cross validation. Furthermore, case studies also indicate that DeepKEGG serves as an effective tool for biomarker discovery. The code is available at https://github.com/lanbiolab/DeepKEGG.

Protein annotation has long been a challenging task in computational biology. Gene Ontology (GO) has become one of the most popular frameworks to describe protein functions and their relationships. Prediction of a protein annotation with proper GO terms demands high-quality GO term representation learning, which aims to learn a low-dimensional dense vector representation with accompanying semantic meaning for each functional label, also known as embedding. However, existing GO term embedding methods, which mainly take into account ancestral co-occurrence information, have yet to capture the full topological information in the GO-directed acyclic graph (DAG). In this study, we propose a novel GO term representation learning method, PO2Vec, to utilize the partial order relationships to improve the GO term representations. Extensive evaluations show that PO2Vec achieves better outcomes than existing embedding methods in a variety of downstream biological tasks. Based on PO2Vec, we further developed a new protein function prediction method PO2GO, which demonstrates superior performance measured in multiple metrics and annotation specificity as well as few-shot prediction capability in the benchmarks. These results suggest that the high-quality representation of GO structure is critical for diverse biological tasks including computational protein annotation.

With the flourish of digital technologies and rapid development of 5G and beyond networks, Metaverse has become an increasingly hotly discussed topic, which offers users with multiple roles for diversified experience interacting with virtual services. How to capture and model users’ multi-platform or cross-space data/behaviors become essential to enrich people with more realistic and immersed experience in Metaverse-enabled smart applications over 5G and beyond networks. In this study, we propose a Personalized Federated Learning with Model-Contrastive Learning (PFL-MCL) framework, which may efficiently enhance the communication and interaction in human-centric Metaverse environments by making use of the large-scale, heterogeneous, and multi-modal Metaverse data. Differing from the conventional Federated Learning (FL) architecture, a multi-center aggregation structure to learn multiple global models based on the changes of dynamically updated local model weights, is developed in global, while a hierarchical neural network structure which includes a personalized module and a federated module to tackle both issues on data heterogeneity and model heterogeneity, is designed in local, so as to enhance the performance of PFL with unique characteristics of Metaverse data. In particular, a two-stage iterative clustering algorithm with a more precise initialization is developed to facilitate the personalized global aggregation with dynamically updated multiple aggregation centers. A personalized multi-modal fusion network is constructed to greatly reduce the computational cost and feature dimensions from the high-dimensional heterogeneous inputs for more efficient cross-modal fusion, based on a hierarchical shift-window attention mechanism and a newly designed bridge attention mechanism. A MCL scheme is then incorporated to speed up the model convergence with less communication overload between the local federated module and global model, while an embedding layer which effectively enables the delivered global model to better adapt to the local personality in each client is further integrated. Compared with five baseline methods, experiment and evaluation results based on two different real-world datasets demonstrate the excellent performance of our proposed PFL-MCL model in a fine-grain personalized training strategy, toward more efficient communication and networking among human-centric Metaverse enabled smart applications.

With the rapidly development of biotechnology, it is now possible to obtain single-cell multi-omics data in the same cell. However, how to integrate and analyze these single-cell multi-omics data remains a great challenge. Herein, we introduce an interpretable multitask framework (scMoMtF) for comprehensively analyzing single-cell multi-omics data. The scMoMtF can simultaneously solve multiple key tasks of single-cell multi-omics data including dimension reduction, cell classification and data simulation. The experimental results shows that scMoMtF outperforms current state-of-the-art algorithms on these tasks. In addition, scMoMtF has interpretability which allowing researchers to gain a reliable understanding of potential biological features and mechanisms in single-cell multi-omics data.

CircRNA has been shown to be involved in the occurrence of many diseases. Several computational frameworks have been proposed to identify circRNA-disease associations. Despite the existing computational methods have obtained considerable successes, these methods still require to be improved as their performance may degrade due to the sparsity of the data and the problem of memory overflow. We develop a novel computational framework called LGCDA to predict circRNA-disease associations by fusing local and global features to solve the above mentioned problems. First, we construct closed local subgraphs by using k-hop closed subgraph and label the subgraphs to obtain rich graph pattern information. Then, the local features are extracted by using graph neural network (GNN). In addition, we fuse Gaussian interaction profile (GIP) kernel and cosine similarity to obtain global features. Finally, the score of circRNA-disease associations is predicted by using the multilayer perceptron (MLP) based on local and global features. We perform five-fold cross validation on five datasets for model evaluation and our model surpasses other advanced methods.

The study of nicotine addiction mechanism is of great significance in both nicotine withdrawal and brain science. The detection of addiction-related brain connectivity using functional magnetic resonance imaging (fMRI) is a critical step in study of this mechanism. However, it is challenging to accurately estimate addiction-related brain connectivity due to the low-signal-to-noise ratio of fMRI and the issue of small sample size. In this work, a prior-embedding graph generative adversarial network (PG-GAN) is proposed to capture addiction-related brain connectivity accurately. By designing a dual-generator-based scheme, the addiction-related connectivity generator is employed to learn the feature map of addiction connection, while the reconstruction generator is used for sample reconstruction. Moreover, a bidirectional mapping mechanism is designed to maintain the consistency of sample distribution in the latent space so that addiction-related brain connectivity can be estimated more accurately. The proposed model utilizes prior knowledge embeddings to reduce the search space so that the model can better understand the latent distribution for the issue of small sample size. Experimental results demonstrate the effectiveness of the proposed PG-GAN.

Automated driving systems (ADS) have made remarkable progress in recent years, yet their reliability and testability remain as significant challenges. The environmental conditions that ADS face are highly complex and may result in the disruption of autonomous vehicles. In this study, we propose an approach that leverages causal inference theory to analyze the impact of causal factors on automated driving visual modules. Our method uncovers the root key factors that affect visual perception performance. We further establish a Challenging Index to quantitatively characterize the causal effects of the key factors on perception failures. This quantitative index is subsequently utilized to generate risky scenarios. Through extensive experiments on various state-of-the-art automated driving visual algorithms, we demonstrate the effectiveness of the challenge index in evaluating the level of hazard in the deployment environment. Additionally, the proposed “challenge index guided search” method improves test efficiency by up to 8.95 times compared to the baselines while maintaining a balance between coverage diversity and the hazardous level of test scenarios. Our research offers a new perspective for analyzing and evaluating the impact of key factors on visual perception. This contributes to the reduction of test space and efficiency of the generation of high-value test scenarios, ultimately advancing the deployment of safer automated vehicles.

The deployment of edge servers make a significant impact on the service quality of a Mobile Edge Computing (MEC) system. This service quality relies on solving two key sub-problems: 1) interference management between servers 2) the placement of MEC servers. To improve the Quality of Service (QoS), we propose a method based on Graph Partition (GP) and Upper Confidence Bound (UCB) for solving these two sub-problems. Regarding interference management, we use an undirected graph to represent the interference between MEC servers so that the overall graph can be divided into multiple subsets of non-interfering MEC servers. Regarding server placement, we propose a Multiple Choice-Upper Confidence Bound (MC-UCB) algorithm that place an collection of interference aware edge servers in each selection. To evaluate the performance, we define a user's QoS function based on transmission delay, throughput, and user density comprehensively and compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) from previous work. The simulation results show that the performance of the proposed algorithms is improved by more than 4% compared with the GA algorithm and 6% compared with the PSO algorithm.

Multiview subspace clustering (MVSC) has been used to explore the internal structure of multiview datasets by revealing unique information from different views. Most existing methods ignore the consistent information and angular information of different views. In this article, we propose a novel MVSC via low-rank symmetric affinity graph (LSGMC) to tackle these problems. Specifically, considering the consistent information, we pursue a consistent low-rank structure across views by decomposing the coefficient matrix into three factors. Then, the symmetry constraint is utilized to guarantee weight consistency for each pair of data samples. In addition, considering the angular information, we utilize the fusion mechanism to capture the inherent structure of data. Furthermore, to alleviate the effect brought by the noise and the high redundant data, the Schatten p-norm is employed to obtain a low-rank coefficient matrix. Finally, an adaptive information reduction strategy is designed to generate a high-quality similarity matrix for spectral clustering. Experimental results on 11 datasets demonstrate the superiority of LSGMC in clustering performance compared with ten state-of-the-art multiview clustering methods.

Currently, most medical institutions face the challenge of training a unified model using fragmented and isolated data to address disease prediction problems. Although federated learning has become the recognized paradigm for privacy-preserving model training, how to integrate federated learning with fMRI temporal characteristics to enhance predictive performance remains an open question for functional disease prediction. To address this challenging task, we propose a novel Federated Graph Spatio-Temporal (FedGST) framework for brain functional disease prediction. Specifically, anchor sampling is used to process variable-length time series data on local clients. Then dynamic functional connectivity graphs are generated via sliding windows and Pearson correlation coefficients. Next, we propose an InceptionTime model to extract temporal information from the dynamic functional connectivity graphs on the local clients. Finally, the hidden activation variables are sent to a global server. We propose a UniteGCN model on the global server to receive and process the hidden activation variables from clients. Then, the global server returns gradient information to clients for backpropagation and model parameter updating. Client models aggregate model parameters on the local server and distribute them to clients for the next round of training. We demonstrate that FedGST outperforms other federated learning methods and baselines on ABIDE-1 and ADHD200 datasets.

The precise segmentation and tracking of cells in microscopy image sequences play a pivotal role in biomedical research, facilitating the study of tissue, organ, and organism development. However, manual segmentation and tracking of cells is time-consuming and often require professional experiences. Besides, segmenting cells in the images with a low signal-to-noise ratio remains difficult. While deep learning (DL) has become a common method for cell segmentation, few DL- based methods address concurrent cell segmentation and tracking. In this paper, we propose a novel DL approach featuring graph-based tracking for cell segmentation and tracking in microscopy images. We combine Deeplabv3+ for semantic segmentation and ResNet50 for enhanced feature extraction, enabling comprehensive cell detection and instance segmentation. Post-processing, involving non-maxima suppression and outlier detection, refines predictions and produces final segmentation. The tracking method is based on the relative position of graph nodes to track segmented cells, encompassing cell division and apoptosis. We conduct our experiments on the induced pluripotent stem (iPS) cell datasets, and the results show that the segmentation and tracking performance of our method yields superior performance compared to the benchmark models. More specifically, our approach achieved DET values of 0.955 and 0.913, TRA values of 0.951 and 0.906, and SEG values of 0.690 and 0.665 on two iPS dataset videos, respectively. Additionally, the performance assessment encompasses four real microscopy datasets from the Cell Tracking Challenge (CTC). Our method greatly reduces the cost of manual labeling and labor-intensive costs.

Dimensionality reduction through feature selection becomes inevitable to overcome the problem of the Curse of dimensionality. In this article, we propose a feature (gene) selection method for high dimensional gene expression (GE) data through a Multi-objective optimization-based Multi-view Co-Clustering algorithm (named MMCo-Clus). A thorough comparative analysis with existing feature selection algorithms using external/internal evaluation metrics supports our proposed method’s potency.

Recent studies have shed light on the potential of circular RNA (circRNA) as a biomarker for disease diagnosis and as a nucleic acid vaccine. The exploration of these functionalities requires correct circRNA full-length sequences; however, existing assembly tools can only correctly assemble some circRNAs, and their performance can be further improved. Here, we introduce a novel feature known as the junction contig (JC), which is an extension of the back-splice junction (BSJ). Leveraging the strengths of both BSJ and JC, we present a novel method called JCcirc (https://github.com/cbbzhang/JCcirc). It enables efficient reconstruction of all types of circRNA full-length sequences and their alternative isoforms using splice graphs and fragment coverage. Our findings demonstrate the superiority of JCcirc over existing methods on human simulation datasets, and its average F1 score surpasses CircAST by 0.40 and outperforms both CIRI-full and circRNAfull by 0.13. For circRNAs below 400 bp, 400-800 bp, 800 bp-1200 bp and above 1200 bp, the correct assembly rates are 0.13, 0.09, 0.04 and 0.03 higher, respectively, than those achieved by existing methods. Moreover, JCcirc also outperforms existing assembly tools on other five model species datasets and real sequencing datasets. These results show that JCcirc is a robust tool for accurately assembling circRNA full-length sequences, laying the foundation for the functional analysis of circRNAs.

Microbes play a crucial role in human health and disease. Figuring out the relationship between microbes and diseases leads to significant potential applications in disease treatments. It is an urgent need to devise robust and effective computational methods for identifying disease-related microbes. This work proposes a Multi-View Feature Aggregation (MVFA) scheme that integrates the linear and nonlinear features to identify disease-related microbes. We introduce a non-negative matrix tri-factorization (NMTF) model to extract linear features for diseases and microbes. Then we learn another type of linear feature by utilizing a bi-random walk model. The nonlinear feature is obtained by inputting the two kinds of linear features into a capsule neural network. These three types of features describe the associations between diseases and microbes from different views. Finally, considering the complementary of these features, we leverage a logistic regression model to combine the NMTF model predictions, bi-random walk model predictions, and the capsule neural network predictions to obtain the final microbe-disease pair scores. We apply our method to predict human microbe-disease associations on two datasets. Experimental results show that our multi-view model outperforms the state-of-the-art models in recovering missing microbe-disease associations and predicting associations for new microbes. The ablation study shows that aggregating multi-view linear and nonlinear features can improve the prediction performance. Case studies on two diseases, i.e. Type 1 diabetes and Liver cirrhosis, further validate our method effectiveness.

Accumulating evidence has shown that microbes play significant roles in human health and diseases. Therefore, identifying microbe-disease associations is conducive to disease prevention. In this article, a predictive method called TNRGCN is designed for microbe-disease associations based on Microbe-Drug-Disease Network and Relation Graph Convolutional Network (RGCN). First, considering that indirect links between microbes and diseases will be increased by introducing drug related associations, we construct a Microbe-Drug-Disease tripartite network through data processing from four databases including Human Microbe-Disease Association Database (HMDAD), Disbiome Database, Microbe-Drug Association Database (MDAD) and Comparative Toxicoge-nomics Database (CTD). Second, we construct similarity networks for microbes, diseases and drugs via microbe function similarity, disease semantic similarity and Gaussian interaction profile kernel similarity, respectively. Based on the similarity networks, Principal Component Analysis (PCA) is utilized to extract main features of nodes. These features will be input into the RGCN as initial features. Finally, based on the tripartite network and initial features, we design two-layer RGCN to predict microbe-disease associations. Experimental results indicate that TNRGCN achieves best performance in cross validation compared with other methods. Meanwhile, case studies for Type 2 diabetes (T2D), Bipolar disorder and Autism demonstrate the favorable effectiveness of TNRGCN in association prediction.

Assay of single cell transposase-accessible chromatin with high-throughput sequencing (scATAC-seq) have enabled massively profiling of the chromatin accessibility landscape at the single-cell level. The essential step in analyzing scATAC-seq data is to cluster the cells into different clusters and utilize the clustering information in the subsequent downstream analysis. However, there are some challenges in the clustering analysis of scATAC-seq data. For example, scATAC-seq data are often high-dimensional and extremely sparse, as well as featuring high loss rate or noise. In this study, we proposed the scIAC to address these challenges of scATACseq data. In particular, scIAC combines the Student’s t-distribution similarity imputation and the denoising autoencoder based on the Zero-inflated Negative Binomial (ZINB) distribution. The Student’s t-distribution similarity imputation is used to solve the problem of high sparsity and high loss rate. The denoising autoencoder is employ to extract features which are useful for clustering and to reduce data noises. In addition, the self-training soft K-means and pairwise constraints are utilized in the clustering phase to enhance clustering performance. The experimental validation on several datasets shows that the proposed method performed better than other state-of-the-art methods. In conclusion, scIAC is an effective method to accurately cluster and identify cell types in scATAC-seq data.

Parkinson’s disease (PD) is a common neurodegenerative disorder that impairs the patient’s quality of life. Medical imaging technology has provided a variety of neuroimages for PD diagnosis studies. However, how to effectively integrate the rich representations from multi-modality data is still a challenging task. To address this challenging task, we propose a multiplane embedding and spatial-temporal Transformer (MEST) framework for PD diagnosis. Firstly, we project structural magnetic resonance imaging (sMRI) into 2D images from coronal, sagittal and axial directions, respectively. Then, the multi-plane dynamic images are learned by pre-trained VGG11 and attention mechanism for representation learning. Afterwards, time series information of functional magnetic resonance imaging (fMRI) is used to construct dynamic functional connection images. To capture information changes in brain, spatial-temporal connectivity Transformer (SCTransformer) is utilized to address spatial-temporal redundancy and dependencies. To integrate multimodality data, ensemble learning is designed based on majority voting strategy to perform PD diagnosis. We evaluate the proposed method on 279 subjects from an in-house and Parkinsons Progression Markers Initiative (PPMI) dataset. Experimental results show that the MEST achieves promising performance with accuracies of 0.856 and 0.885, and outperforms some state-of-the-art methods.

The correct identification of the driver genes that lead to cancer development is essential for understanding the mechanisms of cancer and developing drugs to treat it. Currently, most computational methods for identifying cancer driver genes are based on a cohort of patients. However, due to the heterogeneity of cancers, patients diagnosed with the same cancers may have different genomic characteristics and present varied clinical symptoms. It requires devising effective methods to identify personalized cancer driver genes in an individual. This work developed a novel method to predict personalized cancer driver genes of a single sample based on graph convolution networks, namely pDriverGCN. pDriverGCN constructed a mutant gene-sample heterogeneous network according to the known driver genes of samples. Then it employed two separate graph convolution network models to learn feature representations for genes and samples by gathering the features of themselves and their neighbors. Finally, pDriverGCN used the feature representations to reconstruct the association matrix between genes and samples through a linear correlation coefficient decoder. We apply our model to identify personalized driver genes of samples on the TCGA datasets. The experimental results show that our model outperforms state-of-the-art methods being evaluated at both population and individual levels.

The traditional approach of wet biological experiments tends to reveal whether there is an association between specific miRNA molecules and diseases, resulting in a lack of reliable negative samples of miRNA-disease associations in existing databases. To deal with the problem that many current computational methods treat unknown miRNA-disease associations in benchmark datasets as negative samples directly, we propose a graph convolutional neural network model, named DNMFGCN-MDA, based on feature extractions of positive samples. Firstly, by only using a dynamic matrix with positive samples, we extracted the potential feature vectors U and V of miRNAs and diseases in low-dimensional space. Then, we combined known miRNA-disease associations with potential feature vectors U and V to construct heterogeneous graph neural networks. Finally, we adopted graph convolutional neural networks to learn the structural features of the heterogeneous graph network and predicted the potential miRNA-disease associations by using linkage prediction. We use a 5-fold cross-validation experiment to evaluate the performance of our mode, and it turns out that our model has an average AUC of 95.23% and 96.07% on the HMDD v2.0 and HMDD v3.2 datasets, respectively, and achieves better-associated performance than that of the comparison method.

Increasing evidences have proved that circRNA plays a significant role in the development of many diseases. In addition, many researches have shown that circRNA can be considered as the potential biomarker for clinical diagnosis and treatment of disease. Some computational methods have been proposed to predict circRNA-disease associations. However, the performance of these methods is limited as the sparsity of low-order interaction information. In this paper, we propose a new computational method (KGANCDA) to predict circRNA-disease associations based on knowledge graph attention network. The circRNA-disease knowledge graphs are constructed by collecting multiple relationship data among circRNA, disease, miRNA and lncRNA. Then, the knowledge graph attention network is designed to obtain embeddings of each entity by distinguishing the importance of information from neighbors. Besides the low-order neighbor information, it can also capture high-order neighbor information from multisource associations, which alleviates the problem of data sparsity. Finally, the multilayer perceptron is applied to predict the affinity score of circRNA-disease associations based on the embeddings of circRNA and disease. The experiment results show that KGANCDA outperforms than other state-of-the-art methods in 5-fold cross validation. Furthermore, the case study demonstrates that KGANCDA is an effective tool to predict potential circRNA-disease associations.

Deep learning is an artificial intelligence technique in which models express geometric transformations over multiple levels. This method has shown great promise in various fields, including drug development. The availability of public structure databases prompted the researchers to use generative artificial intelligence models to narrow down their search of the chemical space, a novel approach to chemogenomics and de novo drug development. In this study, we developed a strategy that combined an accelerated LSTM_Chem (long short-term memory for de novo compounds generation), dense fully convolutional neural network (DFCNN), and docking to generate a large number of de novo small molecular chemical compounds for given targets. To demonstrate its efficacy and applicability, six important targets that account for various human disorders were used as test examples. Moreover, using the M protease as a proof-of-concept example, we find that iteratively training with previously selected candidates can significantly increase the chance of obtaining novel compounds with higher and higher predicted binding affinities. In addition, we also check the potential benefit of obtaining reliable final de novo compounds with the help of MD simulation and metadynamics simulation. The generation of de novo compounds and the discovery of binders against various targets proposed here would be a practical and effective approach. Assessing the efficacy of these top de novo compounds with biochemical studies is promising to promote related drug development.

Abstract Deep neural networks have achieved great success in both computer vision and natural language processing tasks. How to improve the gradient flows is crucial in training very deep neural networks. To address this challenge, a gradient enhancement approach is proposed through constructing the short circuit neural connections. The proposed short circuit is a unidirectional neural connection that back propagates the sensitivities rather than gradients in neural networks from the deep layers to the shallow layers. Moreover, the short circuit is further formulated as a gradient truncation operation in its connecting layers, which can be plugged into the backbone models without introducing extra training parameters. Extensive experiments demonstrate that the deep neural networks, with the help of short circuit connection, gain a large margin of improvement over the baselines on both computer vision and natural language processing tasks. The work provides the promising solution to the low‐resource scenarios, such as, intelligence transport systems of computer vision, question answering of natural language processing.

Unmanned aerial vehicles (UAVs) are being used in different emerging domains for accomplishing many critical tasks. However, due to the various constraints, such as battery life, computational resources, etc., a UAV under a mission (M-UAV) often needs assistance from an edge/cloud server that is reachable from the M-UAV’s location. A connection between an M-UAV and edge server can be established via an access point or AP. Therefore, before sharing any sensitive information with the edge server, it is essential for an M-UAV to determine the legitimacy of the selected AP. Recently, some works in this direction indicate that a rogue UAV (R-UAV) can successfully mimic a legitimate AP for intercepting the communication channel. Hence, there should be a robust detection mechanism in place for addressing such a threat scenario. In this article, considering one of the emerging domains—the Internet of Vehicle (IoV) networks, at first, we show that communication in the IoV networks can get benefit from the presence of M-UAVs. However, as the link between the M-UAV and edge server can be intercepted by an R-UAV, the adversary may access the sensitive information from the IoV networks. Followed by this, we propose atiming-basedalgorithm for identifying the presence of rogue APs (or R-UAVs) in the channel. The M-UAV executes the timing-based algorithm, and the detection methoddoes notrequire any auxiliary hardware or any modification to the network protocols for meeting the objective. Supported by an extensive evaluation study, we show that without any rigid restriction on the M-UAV’s speed (e.g., by limiting it to almost static) the proposed approach significantly enhances the detection accuracy (at least by a margin of 29.7% and 16.65%) compared to the state-of-the-art methods.

Password is one of the most well-known authentication methods in accessing many Internet of Things (IoT) devices. The usage of passwords, however, inherits several drawbacks and emerging vulnerabilities in the IoT platform. However, many solutions have been proposed to tackle these limitations. Most of these defense strategies suffer from a lack of computational power and memory capacity and do not have immediate cover in the IoT platform. Motivated by this consideration, the goal of this article is fivefold. First, we analyze the feasibility of implementing a honeyword-based defense strategy to prevent the latest developed server-side threat on the IoT domain’s password. Second, we perform thorough cryptanalysis of a recently developed honeyword-based method to evaluate its advancement in preventing the threat and explore the best possible way to incorporate it in the IoT platform. Third, we verify that we can add a honeyword-based solution to the IoT infrastructure by ensuring specific guidelines. Fourth, we propose a generic attack model, namely,matching attackutilizing the compromised password file to perform the security check of any legacy-UI approach for meeting the all essential flatness security criterion. Last, we compare the matching attack’s performance with the corresponding one of a benchmark technological methods over the legacy-UI model and confirm that our attack has 5%–22% more vulnerable than others.

The smart construction site is developing rapidly with the intelligentization of industrial management. Intelligent devices are being widely deployed in construction industry to support artificial intelligence applications. Video surveillance is a core function of smart construction, which demands both high accuracy and low latency. The challenge is that the computation and networking resources in a construction site are often limited, and the inefficient scheduling policies create congestions in the network and bring additional delay that is unbearable to realtime surveillance. Adaptive video configuration and edge computing have been proposed to improve accuracy and reduce latency with limited resources. However, optimizing the video configuration and task scheduling in edge computing involves several factors that often interfere with each other, which significantly decreases the performance of video surveillance. In this article, we present an edge-based solution of video surveillance in the smart construction site assisted by a graph neural network. It leverages the distributed computing model to realize flexible allocation of resources. A graph-assisted hierarchical reinforcement learning algorithm is developed to illustrate the feature of the mobile-edge network and optimize the scheduling policy by the Deep-$Q$Network. We implement and test the proposed solution in the commercial residential buildings of a fortune global 500 real estate company and observe that the proposed algorithm is efficient to maintain a reliable accuracy and keep lower delay. We further conduct a case study to demonstrate the superiority of the proposed solution by comparing it with traditional mechanisms.

We have designed and developed OceanBase, a distributed relational database system from the very basics for a decade. Being a scale-out multi-tenant system, OceanBase is cross-region fault tolerant, which is based on the shared-nothing architecture. Besides sharing many similar goals with alternative distributed DBMS, such as horizontal scalability, fault-tolerance, etc., our design has been driven by the demands of typical RDBMS compatibility as well as both on-premise and off-premise deployments. OceanBase has fulfilled its design goal. It implements the salient features of certain mainstream classical RDBMS, and most applications on them can run on OceanBase, with or without a few minor modifications. Tens of thousands of OceanBase servers have been deployed in Alipay.com as well as many other commercial organizations. It has also successfully passed the TPC-C benchmark test and seized the first place with more than 707 million tpmC. This paper presents the goals, design criteria, infrastructure, and key components of OceanBase including its engines for storage and transaction processing. Further, it details how OceanBase achieves the above leading TPC-C benchmark in a distributed cluster with more than 1,500 servers from 3 zones. It also describes lessons what we have learnt in building OceanBase for more than a decade.

An exhaustive literature survey shows that finding protein/gene similarity is an important step towards solving widespread bioinformatics problems, such as predicting protein-protein interactions, analyzing Protein-Protein Interaction Networks (PPINs), gene prioritization, and disease gene/protein detection. In this article, we have proposed an improved 3-in-1 fused protein similarity measure called FuSim-II. It is built upon combining the weighted average of biological knowledge extracted from three potential genomic/ proteomic resources such as Gene Ontology (GO), PPIN, and protein sequence. Furthermore, we have shown the application of the proposed measure in detecting potential hub-proteins from a given PPIN. Aiming that, we have proposed a multi-objective clustering-based protein hub detection framework with FuSim-II working as the underlying proximity measure. The PPINs of H. Sapiens and M. Musculus organisms are chosen for experimental purposes. Unlike most of the existing hub-detection methods, the proposed technique does not require to follow any protein degree cut-off or threshold to define hubs. A thorough assessment of efficiency between proposed and existing eight protein similarity measures along with eight single/multi-objective clustering methods has been carried out. Internal cluster validity indices like Silhouette and Davies Bouldin (DB) are deployed to accomplish analytical study. Also, a comparative performance analysis between proposed and five existing hub-proteins detection algorithms is conducted through the enrichment of essentiality study. The reported results show the improved performance of FuSim-II over existing protein similarity measures in terms of identifying functionally related proteins as well as relevant hub-proteins. Supplementary material is available at http://csse.szu.edu.cn/staff/cuilz/eng/index.html.

The accurate prediction of glioma grade before surgery is essential for treatment planning and prognosis. Since the gold standard (i.e., biopsy)for grading gliomas is both highly invasive and expensive, and there is a need for a noninvasive and accurate method. In this study, we proposed a novel radiomics-based pipeline by incorporating the intratumoral and peritumoral features extracted from preoperative mpMRI scans to accurately and noninvasively predict glioma grade. To address the unclear peritumoral boundary, we designed an algorithm to capture the peritumoral region with a specified radius. The mpMRI scans of 285 patients derived from a multi-institutional study were adopted. A total of 2153 radiomic features were calculated separately from intratumoral volumes (ITVs)and peritumoral volumes (PTVs)on mpMRI scans, and then refined using LASSO and mRMR feature ranking methods. The top-ranking radiomic features were entered into the classifiers to build radiomic signatures for predicting glioma grade. The prediction performance was evaluated with five-fold cross-validation on a patient-level split. The radiomic signatures utilizing the features of ITV and PTV both show a high accuracy in predicting glioma grade, with AUCs reaching 0.968. By incorporating the features of ITV and PTV, the AUC of IPTV radiomic signature can be increased to 0.975, which outperforms the state-of-the-art methods. Additionally, our proposed method was further demonstrated to have strong generalization performance in an external validation dataset with 65 patients. The source code of our implementation is made publicly available at https://github.com/chengjianhong/glioma_grading.git.

As is well known, biological experiments are time-consuming and laborious, so there is absolutely no doubt that developing an effective computational model will help solve these problems. Most of computational models rely on the biological similarity and network-based methods that cannot consider the topological structures of metabolite-disease association graphs. We proposed a novel method based on graph convolutional networks to infer potential metabolite-disease association, named MDAGCN. We first calculated three kinds of metabolite similarities and three kinds of disease similarities. The final similarity of disease and metabolite will be obtained by integrating three kinds' similarities of each and filtering out the noise similarity values. Then metabolite similarity network, disease similarity network and known metabolite-disease association network were used to construct a heterogenous network. Finally, heterogeneous network with rich information is fed into the graph convolutional networks to obtain new features of a node through aggregation of node information so as to infer the potential associations between metabolites and diseases. Experimental results show that MDAGCN achieves more reliable results in cross validation and case studies when compared with other existing methods.

MOTIVATION: The interactions of proteins with DNA, RNA, peptide, and carbohydrate play key roles in various biological processes. The studies of uncharacterized protein-molecules interactions could be aided by accurate predictions of residues that bind with partner molecules. However, the existing methods for predicting binding residues on proteins remain of relatively low accuracies due to the limited number of complex structures in databases. As different types of molecules partially share chemical mechanisms, the predictions for each molecular type should benefit from the binding information with other molecule types. RESULTS: In this study, we employed a multiple task deep learning strategy to develop a new sequence-based method for simultaneously predicting binding residues/sites with multiple important molecule types named MTDsite. By combining four training sets for DNA, RNA, peptide, and carbohydrate-binding proteins, our method yielded accurate and robust predictions with AUC values of 0.852, 0836, 0.758, and 0.776 on their respective independent test sets, which are 0.52 to 6.6% better than other state-of-the-art methods. To my best knowledge, this is the first method using multi-task framework to predict multiple molecular binding sites simultaneously.

Numerous microbes have been found to have vital impacts on human health through affecting biological processes. Therefore, exploring potential associations between microbes and diseases will promote the understanding and diagnosis of diseases. In this study, we present a novel computational model, named MSLINE, to infer potential microbe-disease associations by integrating Multiple Similarities and Large-scale Information Network Embedding (LINE) based on known associations. Specifically, on the basis of known microbe-disease associations from the Human Microbe-Disease Association Database, we first increase the known associations by collecting proven associations from existing literatures. We then construct a microbe-disease heterogeneous network (MDHN) by integrating known associations and multiple similarities (including Gaussian interaction profile kernel similarity, microbe function similarity, disease semantic similarity and disease-symptom similarity). After that, we implement random walk and LINE algorithm on MDHN to learn its structure information. Finally, we score the microbe-disease associations according to the structure information for every nodes. In the Leave-one-out cross validation and 5-fold cross validation, MSLINE performs better compared to other existing methods. Moreover, case studies of different diseases proved that MSLINE could predict the potential microbe-disease associations efficiently.

A drug-drug interaction (DDI) is defined as an association between two drugs where the pharmacological effects of a drug are influenced by another drug. Positive DDIs can usually improve the therapeutic effects of patients, but negative DDIs cause the major cause of adverse drug reactions and even result in the drug withdrawal from the market and the patient death. Therefore, identifying DDIs has become a key component of the drug development and disease treatment. In this study, we propose a novel method to predict DDIs based on the integrated similarity and semi-supervised learning (DDI-IS-SL). DDI-IS-SL integrates the drug chemical, biological and phenotype data to calculate the feature similarity of drugs with the cosine similarity method. The Gaussian Interaction Profile kernel similarity of drugs is also calculated based on known DDIs. A semi-supervised learning method (the Regularized Least Squares classifier) is used to calculate the interaction possibility scores of drug-drug pairs. In terms of the 5-fold cross validation, 10-fold cross validation and de novo drug validation, DDI-IS-SL can achieve the better prediction performance than other comparative methods. In addition, the average computation time of DDI-IS-SL is shorter than that of other comparative methods. Finally, case studies further demonstrate the performance of DDI-IS-SL in practical applications.

Along with the popularity of the Internet of Things (IoT) techniques with several computational paradigms, such as cloud and edge computing, microservice has been viewed as a promising architecture in large-scale application design and deployment. Due to the limited computing ability of edge devices in distributed IoT, only a small scale of data can be used for model training. In addition, most of the machine-learning-based intrusion detection methods are insufficient when dealing with imbalanced dataset under limited computing resources. In this article, we propose an optimized intra/inter-class-structure-based variational few-shot learning (OICS-VFSL) model to overcome a specific out-of-distribution problem in imbalanced learning, and to improve the microservice-oriented intrusion detection in distributed IoT systems. Following a newly designed VFSL framework, an intra/inter-class optimization scheme is developed using reconstructed feature embeddings, in which the intra-class distance is optimized based on the approximation during a variation Bayesian process, while the inter-class distance is optimized based on the maximization of similarities during a feature concatenation process. An intelligent intrusion detection algorithm is, then, introduced to improve the multiclass classification via a nonlinear neural network. Evaluation experiments are conducted using two public datasets to demonstrate the effectiveness of our proposed model, especially in detecting novel attacks with extremely imbalanced data, compared with four baseline methods.

In the era of Big Data, cluster analysis of high-dimensional data sets often suffers from theCurse of dimensionality. To overcome this problem, the dimensionality reduction throughfeature selectionbecomes inevitable. Co-clustering or two-way clustering is considered to be a more sophisticated tool than conventional one-way clustering. Moreover, the advent of multi-view learning shows that the subjects of a data set can be interpreted in many ways. Interestingly, a minimal number of existing feature selection algorithms take advantage of the co-clustering method and are designed to consider multi-view data. Motivated by this, in the current article, we propose a feature (gene) selection method for high dimensional gene expression (GE) data through amulti-objective optimization basedmulti-viewCo-Clustering algorithm (namedMMCo-Clus). A popular evolutionary technique – Non-dominated Sorting Genetic Algorithm-II (NSGA-II) has been utilized as the proposed method's underlying optimization strategy. First, we construct two views of a chosen data set, utilizing knowledge from two different biological data sources. Next, we develop the MMCo-Clusalgorithm considering the constructed views to identify a set of “good” co-clustering solutions. Finally, based on a concept ofconsensus operationon the co-clustering outcome, a small number of most relevant and non-redundant features are extracted from the original feature-space. The reduced dimension formed by new feature-space causes to decrease the computational burden and noise level of original data. For experimental analysis, we have chosen three benchmark GE data sets. Our feature selection method's effectiveness is evaluated through sample-classification accuracy, accompanied by the cluster profile plot/Eisen plot/t-SNE plot, and biological/statistical significance test. A thorough comparative analysis with existing feature selection algorithms using external and internal evaluation metrics supports our proposed method's potency.

Virtual drug screening that provides possible drug candidates facilitates early-stage drug discovery. It works by large scale predicting native-like protein-ligand complexes (PLC) from an abundance of docking decoys. Many affinity predicting models currently in use fail to provide reliable prediction because of a lack of non-binding data during model training, lost critical physical-chemical features, and difficulties in learning abstract information with limited neural layers. In this paper, we developed a deep learning model, DeepBindBC for classifying putative ligands as binding or non-binding. Our model incorporates information of non-binding interactions, making it more suitable for real applications. ResNet model architecture and more detailed atom type representation guarantee implicit features can be learned more accurately. DeepBindBC identified a novel human pancreatic $\alpha$-amylase binder validated by a fluorescence spectral experiment (Ka $=1.0\times 10^{5}\mathrm{M}$). Furthermore, we proposed a virtual screening pipeline by incorporating multiple complementary methods, such as DFCNN, Autodock vina docking, DeepBindBC, and pocket molecular dynamics simulation. Three potential inhibitors of pancreatic $\alpha$-amylase were identified by the proposed pipeline, and interestingly most of them contain glycan groups. Additionally, an online webserver based on the model is available at http://cbblab.siat.ac.cn/DeepBindBC/index.php for the convenience of the users.

The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor cost and production time, nowadays, calculation-based methods, especially machine learning and deep learning methods, have received a lot of attention and been used commonly to solve these problems. From a computational point of view, this survey mainly introduces three common non-coding RNAs, i.e. miRNAs, lncRNAs and circRNAs, and the related computational methods for predicting their association with diseases. First, the mainstream databases of above three non-coding RNAs are introduced in detail. Then, we present several methods for RNA similarity and disease similarity calculations. Later, we investigate ncRNA-disease prediction methods in details and classify these methods into five types: network propagating, recommend system, matrix completion, machine learning and deep learning. Furthermore, we provide a summary of the applications of these five types of computational methods in predicting the associations between diseases and miRNAs, lncRNAs and circRNAs, respectively. Finally, the advantages and limitations of various methods are identified, and future researches and challenges are also discussed.

In the era of the Internet of Things (IoT), people access many applications through smartphones for controlling smart devices. Therefore, such a centralized node must follow a robust access control mechanism so that an intruder cannot control the connected devices. Recent reports suggest that password can be used as an authentication factor for accessing the smart setups. However, this static information can be compromised under the light of different machine learning (ML)-empowered attack mechanisms. Alarmingly, different sensors used in the IoT setup can also expose this static information to the adversaries. Password-based authentication that uses a challenge-response strategy is an effective solution for handling such threat scenarios. In this article, at first, we show that no existing usable challenge-response protocol is safe to be used in the public area network. Following this, we propose a challenge-response protocol that is more secure to use in the public domain. By using eight classifiers, we show that a learning-based threat specific to our protocol has a marginal impact on the method's security standard. The discussion in this article also suggests that the proposed protocol has usability and security advantages compared to the existing state of the art (e.g., reduces the number of interactions between the user and verifier by a factor of 0.5).

Federated Learning (FL) is an emerging decentralized learning framework through which multiple clients can collaboratively train a learning model. However, a major obstacle that impedes the wide deployment of FL lies in massive communication traffic. To train high dimensional machine learning models (such as CNN models), heavy communication traffic can be incurred by exchanging model updates via the Internet between clients and the parameter server (PS), implying that the network resource can be easily exhausted. Compressing model updates is an effective way to reduce the traffic amount. However, a flexible unbiased compression algorithm applicable for both uplink and downlink compression in FL is still absent from existing works. In this work, we devise the Model Update Compression by Soft Clustering (MUCSC) algorithm to compress model updates transmitted between clients and the PS. In MUCSC, it is only necessary to transmit cluster centroids and the cluster ID of each model update. Moreover, we prove that: 1) The compressed model updates are unbiased estimation of their original values so that the convergence rate by transmitting compressed model updates is unchanged; 2) MUCSC can guarantee that the influence of the compression error on the model accuracy is minimized. Then, we further propose the boosted MUCSC (B-MUCSC) algorithm, a biased compression algorithm that can achieve an extremely high compression rate by grouping insignificant model updates into a super cluster. B-MUCSC is suitable for scenarios with very scarce network resource. Ultimately, we conduct extensive experiments with the CIFAR-10 and FEMNIST datasets to demonstrate that our algorithms can not only substantially reduce the volume of communication traffic in FL, but also improve the training efficiency in practical networks.

The novel coronavirus (COVID-19) infections have adopted the shape of a global pandemic now, demanding an urgent vaccine design. The current work reports contriving an anti-coronavirus peptide scanner tool to discern anti-coronavirus targets in the embodiment of peptides. The proffered CoronaPep tool features the fast fingerprinting of the anti-coronavirus target serving supreme prominence in the current bioinformatics research. The anti-coronavirus target protein sequences reported from the current outbreak are scanned against the anti-coronavirus target data-sets via CORONAPEP which provides precision-based anti-coronavirus peptides. This tool is specifically for the coronavirus data, which can predict peptides from the whole genome, or a gene or protein's list. Besides it is relatively fast, accurate, userfriendly and can generate maximum output from the limited information. The availability of tools like CORONAPEP will immeasurably perquisite researchers in the discipline of oncology and structure-based drug design.

Assembling genomes from single-cell sequencing data is essential for single-cell studies. However, single-cell assemblies are challenging due to (i) the highly non-uniform read coverage and (ii) the elevated levels of sequencing errors and chimeric reads. Although several assemblers for single-cell data have been proposed in recent years, most of them fail to construct correct long contigs. In this study, we present a new framework called EPGA-SC for de novo assembly of single-cell sequencing reads. The EPGA assembler has designed strategies to solve the problems caused by sequencing errors, sequencing biases, and repetitive regions. However, the extremely unbalanced and richer error types prevent EPGA to achieve high performance in single-cell sequencing data. In this study, we designed EPGA-SC based on EPGA. The main innovations of EPGA-SC are as follows: (i) classifying reads to reduce the proportion of false reads; (ii) using multiple sets of high precision paired-end reads generated from the high precision assemblies produced by other assembler such as SPAdes to overcome the impact of sequencing biases and repetitive regions; and (iii) developing novel algorithms for removing chimeric errors and extending contigs. We test EPGA-SC with seven datasets. The experimental results show that EPGA-SC can generate better assemblies than most current tools in most time in term of MAX contig, N50, NG50, NA50, and NGA50.

Computational drug repositioning, which is an efficient approach to find potential indications for drugs, has been used to increase the efficiency of drug development. The drug repositioning problem essentially is a top-K recommendation task that recommends most likely diseases to drugs based on drug and disease related information. Therefore, many recommendation methods can be adopted to drug repositioning. Collaborative metric learning (CML) algorithm can produce distance metrics that capture the important relationships among objects, and has been widely used in recommendation domains. By applying CML in drug repositioning, a joint metric space is learned to encode drug's relationships with different diseases. In this study, we propose a novel drug repositioning computational method using Collaborative Metric Learning to predict novel drug-disease associations based on known drug and disease related information. Specifically, the proposed method learns latent vectors of drugs and diseases by applying metric learning, and then predicts the association probability of one drug-disease pair based on the learned vectors. The comprehensive experimental results show that CMLDR outperforms the other state-of-the-art drug repositioning algorithms in terms of precision, recall, and AUPR.

The papers in this special section focus on computational genomics and molecular medicine for emerging COVID-19. In 2020, World Health Organization announced Coronavirus disease (COVID)-19 is a pandemic disease, which is devastated the socio-economic life around the world. The disease caused by the zoonotic single-strand RNA virus known as “SARS-CoV-2”. To overcome the pandemic, the diagnosis and therapeutics products needs to be developed in short term. Developing therapeutics for infectious diseases, especially viral diseases always a challenging task for the scientific community. However, the utility of high-performance computational resources, artificial intelligence, and machine-learning algorithms can make the process in an affordable way through the usage of genomics, proteomics, pharmacogenomics, and chemical data. Thus, the special section received potential research articles related to computational genomics, molecular medicine, and COVID-19 from reputed scientist around the world. Different articles were employed machine learning, molecular dynamics, computer aided drug design techniques, and emphasizing viral genomics, mutation, drug target, drug candidates, and patient data, were included in this special section.

Single-cell RNA sequencing (scRNA-seq) technology provides quantitative gene expression profiles at single-cell resolution. As a result, researchers have established new ways to explore cell population heterogeneity and genetic variability of cells. One of the current research directions for scRNA-seq data is to identify different cell types accurately through unsupervised clustering methods. However, scRNA-seq data analysis is challenging because of their high noise level, high dimensionality and sparsity. Moreover, the impact of multiple latent factors on gene expression heterogeneity and on the ability to accurately identify cell types remains unclear. How to overcome these challenges to reveal the biological difference between cell types has become the key to analyze scRNA-seq data. For these reasons, the unsupervised learning for cell population discovery based on scRNA-seq data analysis has become an important research area. A cell similarity assessment method plays a significant role in cell clustering. Here, we present BioRank, a new cell similarity assessment method based on annotated gene sets and gene ranks. To evaluate the performances, we cluster cells by two classical clustering algorithms based on the similarity between cells obtained by BioRank. In addition, BioRank can be used by any clustering algorithm that requires a similarity matrix. Applying BioRank to 12 public scRNA-seq datasets, we show that it is better than or at least as well as several popular similarity assessment methods for single cell clustering.

With the development of high-through sequencing technology and microbiology, many studies have evidenced that microbes are associated with human diseases, such as obesity, liver cancer, and so on. Therefore, identifying the association between microbes and diseases has become an important study topic in current bioinformatics. The emergence of microbe-disease association database has provided an unprecedented opportunity to develop computational method for predicting microbe-disease associations. In the study, we propose a low-rank matrix completion method (called MCHMDA) to predict microbe-disease associations by integrating similarities of microbes and diseases and known microbe-disease associations into a heterogeneous network. The microbe similarity is computed from Gaussian Interaction Profile (GIP) kernel similarity based on the known microbe-disease associations. Then, we further improve the microbe similarity by taking into account the inhabiting organs of these microbes in human body. The disease similarity is computed by the average of disease GIP similarity, disease symptom-based similarity, and disease functional similarity. Then, we construct a heterogeneous microbe-disease association network by integrating the microbe similarity network, disease similarity network, and known microbe-disease association network. Finally, a matrix completion method is used to calculate the association scores of unknown microbe-disease pairs by the fast Singular Value Thresholding (SVT) algorithm. Via 5-fold Cross Validation (5CV) and Leave-One-Out Cross Validation (LOOCV), we evaluate the prediction performances of MCHMDA and other state-of-the-art methods which include BRWMDA, NGRHMDA, LRLSHMDA, and KATZHMDA. On benchmark dataset HMDAD, the experimental results show that MCHMDA outperforms other methods in terms of area under the receiver operating characteristic curve (AUC). MCHMDA achieves the AUC values of 0.9251 and 0.9495 in 5CV and LOOCV, respectively, which are the highest values among the competing methods. In addition, we also further indicate the prediction generality of MCHMDA on an expanded microbe-disease associations dataset (HMDAD-SUP). Finally, case studies prove the prediction ability in practical applications.

Computational methods including centrality and machine learning-based methods have been proposed to identify essential proteins for understanding the minimum requirements of the survival and evolution of a cell. In centrality methods, researchers are required to design a score function which is based on prior knowledge, yet is usually not sufficient to capture the complexity of biological information. In machine learning-based methods, some selected biological features cannot represent the complete properties of biological information as they lack a computational framework to automatically select features. To tackle these problems, we propose a deep learning framework to automatically learn biological features without prior knowledge. We use node2vec technique to automatically learn a richer representation of protein-protein interaction (PPI) network topologies than a score function. Bidirectional long short term memory cells are applied to capture non-local relationships in gene expression data. For subcellular localization information, we exploit a high dimensional indicator vector to characterize their feature. To evaluate the performance of our method, we tested it on PPI network of S. cerevisiae. Our experimental results demonstrate that the performance of our method is better than traditional centrality methods and is superior to existing machine learning-based methods. To explore which of the three types of biological information is the most vital element, we conduct an ablation study by removing each component in turn. Our results show that the PPI network embedding contributes most to the improvement. In addition, gene expression profiles and subcellular localization information are also helpful to improve the performance in identification of essential proteins.

Homozygous and heterozygous deletions commonly exist in the human genome. For current structural variation detection tools, it is significant to determine whether a deletion is homozygous or heterozygous. However, the problems of sequencing errors, micro-homologies, and micro-insertions prohibit common alignment tools from identifying accurate breakpoint locations, and often result in detecting false structural variations. In this study, we present a novel deletion detection tool called Sprites2. Comparing with Sprites, Sprites2 makes the following modifications: (1) The distribution of insert size is used in Sprites2, which can identify the type of deletions and improve the accuracy of deletion calls. (2) A precise alignment method based on AGE (one algorithm simultaneously aligning 5' and 3' ends between two sequences) is adopted in Sprites2 to identify breakpoints, which is helpful to resolve the problems introduced by sequencing errors, micro-homologies, and micro-insertions. In order to test and verify the performance of Sprites2, some simulated and real datasets are adopted in our experiments, and Sprites2 is compared with five popular tools. The experimental results show that Sprites2 can improve the performance of deletion detection. Sprites2 can be downloaded from https://github.com/zhangzhen/sprites2.

Knowledge of protein functions plays an important role in biology and medicine. With the rapid development of high-throughput technologies, a huge number of proteins have been discovered. However, there are a great number of proteins without functional annotations. A protein usually has multiple functions and some functions or biological processes require interactions of a plurality of proteins. Additionally, Gene Ontology provides a useful classification for protein functions and contains more than 40,000 terms. We propose a deep learning framework called DeepGOA to predict protein functions with protein sequences and protein-protein interaction (PPI) networks. For protein sequences, we extract two types of information: sequence semantic information and subsequence-based features. We use the word2vec technique to numerically represent protein sequences, and utilize a Bi-directional Long and Short Time Memory (Bi-LSTM) and multi-scale convolutional neural network (multi-scale CNN) to obtain the global and local semantic features of protein sequences, respectively. Additionally, we use the InterPro tool to scan protein sequences for extracting subsequence-based information, such as domains and motifs. Then, the information is plugged into a neural network to generate high-quality features. For the PPI network, the Deepwalk algorithm is applied to generate its embedding information of PPI. Then the two types of features are concatenated together to predict protein functions. To evaluate the performance of DeepGOA, several different evaluation methods and metrics are utilized. The experimental results show that DeepGOA outperforms DeepGO and BLAST.

Gene regulatory networks (GRNs) play a key role in biological processes. However, GRNs are diverse under different biological conditions. Reconstructing gene regulatory networks (GRNs) from gene expression has become an important opportunity and challenge in the past decades. Although there are a lot of existing methods to infer the topology of GRNs, such as mutual information, random forest, and partial least squares, the accuracy is still low due to the noise and high dimension of the expression data. In this paper, we introduce an ensemble Multivariate Adaptive Regression Splines (MARS) based method to reconstruct the directed GRNs from multifactorial gene expression data, called PBMarsNet. PBMarsNet incorporates part mutual information (PMI) to pre-weight the candidate regulatory genes and then uses MARS to detect the nonlinear regulatory links. Moreover, we apply bootstrap to run the MARS multiple times and average the outputs of each MARS as the final score of regulatory links. The results on DREAM4 challenge and DREAM5 challenge datasets show PBMarsNet has a superior performance and generalization over other state-of-the-art methods.

In recent years, vaccine incidents occurred around the world, which endangers people’s lives. In the technical respect, these incidents are partially due to the fact that existing vaccine management systems are distributively managed by different entities in the vaccine supply chain. This architecture makes it relatively easy to modify or even delete the vaccine circulation data maliciously, which makes tracing problematic vaccine hard and identifying the responsibility for a vaccine accident hard. To solve these issues, this article presents a blockchain-based solution to protect the whole process of vaccine circulation. We first propose a model to supervise the vaccine circulation process by incorporating existing regulatory practices. Then, we propose a blockchain-based tracing system to implement this model. The proposed system takes the blockchain as a global, unique, and verifiable database to store all the circulation data. Through data insertions and queries on the global and unique database, the proposed system achieves the protection of vaccine circulation. We also implement a proof-of-concept prototype of the proposed system. Experimental results confirm that the proposed system is beneficial.

The cause of Alzheimer's disease (AD) is insufficient to understand so far, and its diagnosis is challenging in clinical practice. Recently, the convolutional neural network (CNN) model has shown impressive performance in medical image analysis. Combining CNN with magnetic resonance imaging (MRI) image has excellent potential for AD diagnosis. However, it is still a challenging task. To address the challenge, we propose a joint learning method based on multi-scale representation (JL-MSR). The multi-scale representation is proposed to obtain more feature maps by the multi-scale atrous convolutions. Furthermore, in order to use the intrinsic relationship between diagnostic results and clinical scores, we propose a joint learning strategy using the diagnosis result as the primary label and the Mini-Mental State Examination (MMSE) score as the secondary label to joint training. The proposed method is evaluated on a dataset of 417 subjects (including 188 AD and 229 health controls (HC)) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The experimental results show that our proposed method achieves an accuracy of 88.1% and an area under the receiver operating characteristic (ROC) curve (AUC) value of 0.942 for AD diagnosis, respectively. Compared with a state-of-the-art method in AD diagnosis, our proposed method performs better, and has potential in clinical diagnosis.

Microbes play a crucial role in human health and disease. Understanding the relationship between microbes and diseases is conducive to the treatment and diagnosis of diseases. Recently, many computational methods have been proposed to predict disease-microbe associations. However, most of the existing methods only consider a single model and explore the disease-microbe associations from a single view. To improve the prediction accuracy, we propose a novel multi-view approach that fuses the linear and nonlinear features to predict new potential associations between diseases and microbes. We first design a non-negative matrix tri-factorization method to extract the linear features of diseases and microbes. We input the linear features from the non-negative matrix tri-factorization model and bi-random walk model into a capsule neural network to obtain the diseases and microbes' nonlinear features. Finally, we leverage a logistic regression model to combine the non-negative matrix tri-factorization model predictions, bi-random walk model predictions and the capsule neural network predictions to obtain the final association scores between microbes and diseases. We apply our method to predict human microbedisease associations. Experimental results show that our fusion model outperforms the non-negative matrix tri-factorization model, bi-random walk model and other existing models.

Graphs can be used to effectively represent complex data structures. Learning these irregular data in graphs is challenging and still suffers from shallow learning. Applying deep learning on graphs has demonstrated good performance in many applications including social analysis, bioinformatics etc. Message passing graph convolution network is a powerful method which has expressive power to learn graph structures. Meanwhile, circular ribonucleic acid (circRNA) is a type of non-coding RNA which plays a critical role in human diseases. Identifying the associations between circRNAs and diseases is important for diagnosis and treatment of complex diseases. However, there are limited number of known associations between them and conducting biological experiments to identify new associations is time consuming and expensive. As a result, there is a need of building efficient and feasible computation methods to predict potential circRNA-disease associations. In this paper, we propose a novel graph convolution network framework to learn features from a graph built with multi-source similarity information to predict circRNA-disease associations. First we use multi-source information of circRNA similarity, disease and circRNA Gaussian Interaction Profile (GIP) kernel similarity to extract the features using first graph convolution. Then we predict disease associations for each circRNA with a second graph convolution. Proposed framework with five-fold cross validation on various experiments shows promising results in predicting circRNA-disease association and outperforms other existing methods.

BACKGROUND: In the field of computational biology, analyzing complex data helps to extract relevant biological information. Sample classification of gene expression data is one such popular bio-data analysis technique. However, the presence of a large number of irrelevant/redundant genes in expression data makes a sample classification algorithm working inefficiently. Feature selection is one such high-dimensionality reduction technique that helps to maximize the effectiveness of any sample classification algorithm. Recent advances in biotechnology have improved the biological data to include multi-modal or multiple views. Different 'omics' resources capture various equally important biological properties of entities. However, most of the existing feature selection methodologies are biased towards considering only one out of multiple biological resources. Consequently, some crucial aspects of available biological knowledge may get ignored, which could further improve feature selection efficiency. RESULTS: In this present work, we have proposed a Consensus Multi-View Multi-objective Clustering-based feature selection algorithm called CMVMC. Three controlled genomic and proteomic resources like gene expression, Gene Ontology (GO), and protein-protein interaction network (PPIN) are utilized to build two independent views. The concept of multi-objective consensus clustering has been applied within our proposed gene selection method to satisfy both incorporated views. Gene expression data sets of Multiple tissues and Yeast from two different organisms (Homo Sapiens and Saccharomyces cerevisiae, respectively) are chosen for experimental purposes. As the end-product of CMVMC, a reduced set of relevant and non-redundant genes are found for each chosen data set. These genes finally participate in an effective sample classification. CONCLUSIONS: The experimental study on chosen data sets shows that our proposed feature-selection method improves the sample classification accuracy and reduces the gene-space up to a significant level. In the case of Multiple Tissues data set, CMVMC reduces the number of genes (features) from 5565 to 41, with 92.73% of sample classification accuracy. For Yeast data set, the number of genes got reduced to 10 from 2884, with 95.84% sample classification accuracy. Two internal cluster validity indices - Silhouette and Davies-Bouldin (DB) and one external validity index Classification Accuracy (CA) are chosen for comparative study. Reported results are further validated through well-known biological significance test and visualization tool.

BACKGROUND: In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population. RESULTS: In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select. Three important resources of biological data (gene ontology, protein interaction data, protein sequence) along with gene expression values are collectively utilized to design two different views. UMVMO-select aims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets. CONCLUSION: A thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting.

With the development of Internet of Things (IoT), edge computing becomes more and more prevalent currently. However, edge computing needs to deploy a large number of edge servers to reduce the communication latency, which will bring additional costs to the system. Although there exist some idle computing resources at the edge, the owners distrust each other and lack the incentives to contribute to the system. In this article, we propose a new edge computing platform decentralized and trusted platform for edge computing (DeTEC), which provides a unified interface to users, resolves the user's requests to the most appropriate edge server through domain name server, and returns the computational results to the IoT user. To build a trustworthy system, DeTEC integrates the blockchain technology with edge computing, such that the contributions of each participant could be accounted and rewarded. We formulate the task allocation problem, taking both node capacity and reward fairness into consideration, and solve it through a heuristic algorithm. Finally, to guarantee the trustworthiness of computational results, we utilize a police patrol model and try to optimize the system overall reward. We implement DeTEC based on an open source project and conduct comprehensive experiments to test its performance. The results show that our DeTEC system works well in the IoT scenario.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus poses serious threats to the global public health and leads to worldwide crisis. No effective drug or vaccine is readily available. The viral RNA-dependent RNA polymerase (RdRp) is a promising therapeutic target. A hybrid drug screening procedure was proposed and applied to identify potential drug candidates targeting RdRp from 1906 approved drugs. Among the four selected market available drug candidates, Pralatrexate and Azithromycin were confirmed to effectively inhibit SARS-CoV-2 replication in vitro with EC50 values of 0.008μM and 9.453 μM, respectively. For the first time, our study discovered that Pralatrexate is able to potently inhibit SARS-CoV-2 replication with a stronger inhibitory activity than Remdesivir within the same experimental conditions. The paper demonstrates the feasibility of fast and accurate anti-viral drug screening for inhibitors of SARS-CoV-2 and provides potential therapeutic agents against COVID-19.

microRNAs (miRNAs) are small and important non-coding RNAs that regulate gene expression in transcriptional and post-transcriptional level by combining with their targets (genes). Predicting miRNA targets is an important problem in biological research. It is expensive and time-consuming to identify miRNA targets by using biological experiments. Many computational methods have been proposed to predict miRNA targets. In this study, we develop a novel method, named miRTRS, for predicting miRNA targets based on a recommendation algorithm. miRTRS can predict targets for an isolated (new) miRNA with miRNA sequence similarity, as well as isolated (new) targets for a miRNA with gene sequence similarity. Furthermore, when compared to supervised machine learning methods, miRTRS does not need to select negative samples. We use 10-fold cross validation and independent datasets to evaluate the performance of our method. We compared miRTRS with two most recently published methods for miRNA target prediction. The experimental results have shown that our method miRTRS outperforms competing prediction methods in terms of AUC and other evaluation metrics.

Identifying essential proteins plays an important role in disease study, drug design, and understanding the minimal requirement for cellular life. Computational methods for essential proteins discovery overcome the disadvantages of biological experimental methods that are often time-consuming, expensive, and inefficient. The topological features of protein-protein interaction (PPI) networks are often used to design computational prediction methods, such as Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), and Neighborhood Centrality (NC). However, the prediction accuracies of these individual methods still have space to be improved. Studies show that additional information, such as orthologous relations, helps discover essential proteins. Many researchers have proposed different methods by combining multiple information sources to gain improvement of prediction accuracy. In this study, we find that essential proteins appear in triangular structure in PPI network significantly more often than nonessential ones. Based on this phenomenon, we propose a novel pure centrality measure, so-called Neighborhood Closeness Centrality (NCC). Accordingly, we develop a new combination model, Extended Pareto Optimality Consensus model, named EPOC, to fuse NCC and Orthology information and a novel essential proteins identification method, NCCO, is fully proposed. Compared with seven existing classic centrality methods (DC, BC, IC, CC, SC, EC, and NC) and three consensus methods (PeC, ION, and CSC), our results on S.cerevisiae and E.coli datasets show that NCCO has clear advantages. As a consensus method, EPOC also yields better performance than the random walk model.

The rapid development of proteomics and high-throughput technologies has produced a large amount of Protein-Protein Interaction (PPI) data, which makes it possible for considering dynamic properties of protein interaction networks (PINs) instead of static properties. Identification of protein complexes from dynamic PINs becomes a vital scientific problem for understanding cellular life in the post genome era. Up to now, plenty of models or methods have been proposed for the construction of dynamic PINs to identify protein complexes. However, most of the constructed dynamic PINs just focus on the temporal dynamic information and thus overlook the spatial dynamic information of the complex biological systems. To address the limitation of the existing dynamic PIN analysis approaches, in this paper, we propose a new model-based scheme for the construction of the Spatial and Temporal Active Protein Interaction Network (ST-APIN) by integrating time-course gene expression data and subcellular location information. To evaluate the efficiency of ST-APIN, the commonly used classical clustering algorithm MCL is adopted to identify protein complexes from ST-APIN and the other three dynamic PINs, NF-APIN, DPIN, and TC-PIN. The experimental results show that, the performance of MCL on ST-APIN outperforms those on the other three dynamic PINs in terms of matching with known complexes, sensitivity, specificity, and f-measure. Furthermore, we evaluate the identified protein complexes by Gene Ontology (GO) function enrichment analysis. The validation shows that the identified protein complexes from ST-APIN are more biologically significant. This study provides a general paradigm for constructing the ST-APINs, which is essential for further understanding of molecular systems and the biomedical mechanism of complex diseases.

Due to sequencing bias, sequencing error, and repeat problems, the genome assemblies usually contain misarrangements and gaps. When tackling these problems, current assemblers commonly consider the read libraries as a whole and adopt the same strategy to deal with them. However, if we can divide reads into different categories and take different assembly strategies for different read categories, we expect to reduce the mutual effects on problems in genome assembly and facilitate to produce satisfactory assemblies. In this paper, we present a new pipeline for genome assembly based on read classification (ARC). ARC classifies reads into three categories according to the frequencies of k-mers they contain. The three categories refer to (1) low depth reads, which contain a certain low frequency k-mers and are often caused by sequencing errors or bias; (2) high depth reads, which contain a certain high frequency k-mers and usually come from repetitive regions; and (3) normal depth reads, which are the rest of reads. After read classification, an existing assembler is used to assemble different read categories separately, which is beneficial to resolve problems in the genome assembly. ARC adopts loose assembly parameters for low depth reads, and strict assembly parameters for normal depth and high depth reads. We test ARC using five datasets. The experimental results show that, assemblers combining with ARC can generate better assemblies in terms of NA50, NGA50, and genome fraction.

Next-generation sequencing (NGS) has enabled an exponential growth rate of sequencing data. However, several sequence artifacts, including error reads (base calling errors and small insertions or deletions) and poor quality reads, which can impose significant impact on the downstream sequence processing and analysis. Here, we present PE-Trimmer, a sensitive and special trimming algorithm for NGS sequence. First, PE-Trimmer removes technical sequences in paired-end reads based on the characteristics of low quality reads in NGS data. Second, PE-Trimmer determines the range of reads that need to be trimmed according to the quality score statistics histogram of reads in the library. To improve the accuracy of this algorithm, we design a light-weight and easy-to-explain scoring model to evaluate candidates in the pattern of trimming step. Finally, PE-Trimmer selects the appropriate trimming strategy to process the low quality reads based on the location determined by the scoring model. PE-Trimmer is able to locate and remove adapter residues from the paired-end reads. It is easily configurable and offers superior throughput in the multi-threaded mode. We test PE-Trimmer on five datasets, and compare it with the current five latest methods. The experimental results demonstrate that PE-Trimmer produces more superior results, compared with other trimmers.

With the advances in technologies of sequencing and assembly, draft sequences of more and more genomes are available. However, there commonly exist gaps in these draft sequences which influence various downstream analysis of biological studies. Gap filling methods can shorten the length of gaps and improve the completion of these draft sequences of genomes. Although some gap filling tools have been developed, their effectiveness and accuracy need to be improved. In this study, we develop a novel tool, called GapReduce, which can fill the gaps using the paired reads. For a gap, GapReduce selects the reads whose mate reads are aligned on the left or the right flanking region, and partitions the reads to two sets. Then GapReduce adopts different $k$k values and $k$k-$mer$mer frequency thresholds to iteratively construct De Bruijn graphs, which are used for finding the correct path to fill the gap. For overcoming the branching problems caused by repetitive regions and sequencing errors in the procedure of path selection, GapReduce designs a novel approach that simultaneously considers $k$k-$mer$mer frequency and distribution of paired reads based on the partitioned read sets. We compare the performance of GapReduce with current popular gap filling tools. The experimental results demonstrate that GapReduce can produce satisfactory gap filling results, especially for long insert size datasets. GapReduce is publicly available for downloading at https://github.com/bioinfomaticsCSU/GapReduce.

Quantifying the associations between diseases is now playing an important role in modern biology and medicine. Actually discovering associations between diseases could help us gain deeper insights into pathogenic mechanisms of complex diseases, thus could lead to improvements in disease diagnosis, drug repositioning, and drug development. Due to the growing body of high-throughput biological data, a number of methods have been developed for computing similarity between diseases during the past decade. However, these methods rarely consider the interconnections of genes related to each disease in protein-protein interaction network (PPIN). Recently, the disease module theory has been proposed, which states that disease-related genes or proteins tend to interact with each other in the same neighborhood of a PPIN. In this study, we propose a new method called ModuleSim to measure associations between diseases by using disease-gene association data and PPIN data based on disease module theory. The experimental results show that by considering the interactions between disease modules and their modularity, the disease similarity calculated by ModuleSim has a significant correlation with disease classification of Disease Ontology (DO). Furthermore, ModuleSim outperforms other four popular methods which are all using disease-gene association data and PPIN data to measure disease-disease associations. In addition, the disease similarity network constructed by MoudleSim suggests that ModuleSim is capable of finding potential associations between diseases.

The de novo assembly tools aim at reconstructing genomes from next-generation sequencing (NGS) data. However, the assembly tools usually generate a large amount of contigs containing many misassemblies, which are caused by problems of repetitive regions, chimeric reads, and sequencing errors. As they can improve the accuracy of assembly results, detecting and correcting the misassemblies in contigs are appealing, yet challenging. In this study, a novel method, called MEC, is proposed to identify and correct misassemblies in contigs. Based on the insert size distribution of paired-end reads and the statistical analysis of GC-contents, MEC can identify more misassemblies accurately. We evaluate our MEC with the metrics (NA50, NGA50) on four datasets, compared it with the most available misassembly correction tools, and carry out experiments to analyze the influence of MEC on scaffolding results, which shows that MEC can reduce misassemblies effectively and result in quantitative improvements in scaffolding quality. MEC is publicly available at https://github.com/bioinfomaticsCSU/MEC.

Many current studies have evidenced that microbes play important roles in human diseases. Therefore, discovering the associations between microbes and diseases is beneficial to systematically understanding the mechanisms of diseases, diagnosing, and treating complex diseases. It is well known that finding new potential microbe-disease associations via biological experiments is a time-consuming and expensive process. However, the computation methods can provide an opportunity to effectively predict microbe-disease associations. In recent years, efforts toward predicting microbe-disease associations are not in proportional to the importance of microbes to human diseases. In this study, we develop a method (called BRWMDA) to predict new microbe-disease associations based on similarity and improving bi-random walk on the disease and microbe networks. BRWMDA integrates microbe network, disease network, and known microbe-disease associations into a single network. After calculating the Gaussian Interaction Profile (GIP) kernel similarity of microbes based on known microbe-disease associations, the microbe network is obtained by adjusting the similarity with the logistics function. In addition, the disease network is computed by the similarity network fusion (SNF) method with the symptom-based similarity and the GIP kernel similarity based on known microbe-disease associations. Then, these two networks of microbe and disease are connected by known microbe-disease associations. Based on the assumption that similar microbes are normally associated with similar diseases and vice versa, BRWMDA is employed to predict new potential microbe-disease associations via random walk with different steps on microbe and disease networks, which reasonably uses the similarity of microbe network and disease network. The 5-fold cross validation and Leave One Out Cross Validation (LOOCV) are adopted to assess the prediction performance of our BRWMDA algorithm, as well as other competing methods for comparison. 5-fold cross validation experiments show that BRWMDA obtained the maximum AUC value of 0.9087, which is again superior to other methods of 0.9025(NGRHMDA), 0.8797 (LRLSHMDA), 0.8571 (KATZHMDA), 0.7782 (HGBI), and 0.5629 (NBI). In addition, BRWMDA also outperforms other methods in terms of LOOCV, whose AUC value is 0.9397, which is superior to other methods of 0.9111(NGRHMDA), 0.8909 (LRLSHMDA), 0.8644 (KATZHMDA), 0.7866 (HGBI), and 0.5553 (NBI). Case studies also illustrate that BRWMDA is an effective method to predict microbe-disease associations.

Cloud computing offers a cost-effective and elastic computing paradigm that facilitates large scale data storage and analytics. By deploying virtualization technologies in the datacenter, cloud enables efficient resource management and isolation for various big data applications. Since the hotspots (i.e., overloaded machines) can degrade the performance of these applications, virtual machine migration has been utilized to perform load balancing in the datacenters to eliminate hotspots and guarantee Service Level Agreements (SLAs). However, the previous load balancing schemes make migration decisions based on deterministic resource demand estimation and workload characterization, without considering their stochastic properties. By studying real world traces, we show that the resource demand and workload of virtual machines are highly dynamic and bursty, which can cause these schemes to make inefficient migrations for load balancing. To address this problem, in this paper we propose a stochastic load balancing scheme which aims to provide probabilistic guarantee against the resource overloading with virtual machine migration, while minimizing the total migration overhead. Our scheme effectively addresses the prediction of the distribution of resource demand and the multidimensional resource requirements with stochastic characterization. Moreover, as opposed to the previous works that measure the migration cost without considering the network topology, our scheme explicitly takes into account the distance between the source physical machine and the destination physical machine for a virtual machine migration. The trace-driven experiments show that our scheme outperforms the previous schemes in terms of SLA violation and the migration cost.

The dramatic spread of online social network services, such as Facebook, Twitter, Instagram, and Google+, has led to increasing awareness of the power of incorporating social elements into a variety of data-centric applications. These applications, in recent years, apply various sensors with social media platforms to continuously collect massive data that can be directly associated with human interactions. This phenomenon has led to the creation of numerous social sensing systems, such as Biketastic, BikeNet, CarTel, and Pier, which use social sensors (i.e., users) for a variety of social sensing systems and applications. Social sensing has become an emerging and promising sensing paradigm that relies on the voluntary cooperation of users equipped with embedded or integrated sensors.

Different biomedical computing methods for cancer-specific gene recognition have been developed in recent years. Currently, building an open-box machine learning system to discover explainable knowledge from gene expression data is a difficult research problem due to a large number of genes, a small number of samples, and noise. Fuzzy systems can be used to deal with data ambiguity and noise issues and extract meaningful knowledge from gene data. In this article, we create a new deep fuzzy neural network to handle the uncertainty in gene data to generate useful knowledge for specific disease diagnosis. A new hybrid algorithm is designed to preprocess data and select informative genes for accurate cancer detection. Various experiments using six different cancer datasets indicate that the new method has better and more reliable performance than the other conventional classification methods with different gene selection methods.

Industrial Internet of Things (IIoT) has been widely used in many fields. Meanwhile, blockchain is considered promising to address the issues of the IIoT. However, the current blockchains have a limited throughput. In this article, we devise an efficient and secure blockchain protocol compacted directed acyclic graph (CoDAG) based on a compacted directed acyclic graph, where blocks are organized in levels and width. New-generated blocks in the CoDAG will be placed appropriately and point to those in the previous level, making it a well-connected channel. Transactions in the network will be confirmed in a deterministic period, and the CoDAG keeps a simple data structure at the same time. We also illustrate the attack strategies by adversary, and it is proved that our protocols are resistant to these attacks. Furthermore, we design a CoDAG-based IIoT architecture to improve the efficiency of the IIoT system. Experimental results show that the CoDAG achieves 164× Bitcoin's throughput and 77× Ethererum's throughput.

The papers in this special section focuses on artificial intelligent-driven informatics, sensing, imaging and big data analytics in dealing with the COVID-19 pandemic.

The sustainable growth of bandwidth has been an inevitable tendency in current Data Center Networks (DCN). However, the dramatic expansion of link capacity offers a remarkable challenge to the transport layer protocols of DCN, i.e., how to converge fast and enable data flow to utilize the high bandwidth effectively. Meanwhile, the new protocol should be compatible to the traditional TCP because the applications with old TCP versions are still widely deployed. Therefore, it is important to achieve a trade-off between the aggressiveness and TCP-friendliness in protocol design. In this article, we first empirically investigate why the existing typical data center TCP variants naturally fail to guarantee both fast convergence and TCP friendliness. Then, we design a new transport protocol for DCN, namely Fast and Friendly Converging (FFC), which makes independent decisions and self-adjustment through retrieving the two-dimensional congestion notification from both RTT and ECN. We further present a mathematic model to analyze its competing behavior and converging process. The results from simulation experiments and real implementation show that FFC can achieve fast convergence, thus benefiting the flow completion time. Moreover, when coexisting with the traditional TCP, FFC also presents a moderate behavior, while introducing trivial deployment overhead only at the end-hosts.

The ten papers in this special section are devoted to the most recent developments and research outcomes addressing the related theoretical and practical aspects of computational intelligence solutions in sustainable computing and aims at presenting latest innovative ideas targeted at the corresponding key challenges, either from a methodological or from an application perspective.

Accurate segmentation of glioma from 3D medical images is vital to numerous clinical endpoints. While manual segmentation is subjective and time-consuming, fully automated extraction is quite imperative and challenging due to the intrinsic heterogeneity of tumor structures. In this study, we propose a multi-level glioma segmentation framework, 3D Residual-Attention-Atrous U-Net (RAAU-Net), using 3D U-Net combined attention mechanism with atrous convolution. The 3D RAAU-Net can extract contextual information by combining low- and high-resolution feature maps. The attention mechanism is embedded in each skip connection layer of 3D RAAU-Net to enhance feature representations. Meanwhile, the atrous convolution is adopted in the whole network architecture to incorporate large and rich semantic information. Furthermore, we design a new training scheme to reduce false positives and enhance generalization. Eventually, our proposed segmentation method is evaluated on the validation dataset from the Multimodal Brain Tumor Image Segmentation Challenge (BraTS) 2018 and achieve a competitive result with average Dice score of 88% for the whole tumor, 79% for the tumor core and 73% for the enhancing tumor, respectively. Quantitative results and visual analysis have proven that these improvements in 3D RAAU-Net are effective and achieve a better segmentation accuracy compared with the baseline.

MOTIVATION: Scaffolding is an essential step during the de novo sequence assembly process to infer the direction and order relationships between the contigs and make the sequence assembly results more continuous and complete. However, scaffolding still faces the challenges of repetitive regions in genome, sequencing errors and uneven sequencing depth. Moreover, the accuracy of scaffolding greatly depends on the quality of contigs. Generally, the existing scaffolding methods construct a scaffold graph, and then optimize the graph by deleting spurious edges. Nevertheless, due to the wrong joints between contigs, some correct edges connecting contigs may be deleted. RESULTS: In this study, we present a novel scaffolding method SCOP, which is the first method to classify the contigs and utilize the vertices and edges to optimize the scaffold graph. Specially, SCOP employs alignment features and GC-content of paired reads to evaluate the quality of contigs (vertices), and divide the contigs into three types (True, Uncertain and Misassembled), and then optimizes the scaffold graph based on the classification of contigs together with the alignment of edges. The experiment results on the datasets of GAGE-A and GAGE-B demonstrate that SCOP performs better than 12 other competing scaffolders. AVAILABILITY AND IMPLEMENTATION: SCOP is publicly available for download at https://github.com/bioinfomaticsCSU/SCOP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Reconstructing gene regulatory networks (GRNs) based on gene expression profiles is still an enormous challenge in systems biology. Random forest-based methods have been proved a kind of efficient methods to evaluate the importance of gene regulations. Nevertheless, the accuracy of traditional methods can be further improved. With time-series gene expression data, exploiting inherent time information and high order time lag are promising strategies to improve the power and accuracy of GRNs inference. RESULTS: In this study, we propose a scalable, flexible approach called BiXGBoost to reconstruct GRNs. BiXGBoost is a bidirectional-based method by considering both candidate regulatory genes and target genes for a specific gene. Moreover, BiXGBoost utilizes time information efficiently and integrates XGBoost to evaluate the feature importance. Randomization and regularization are also applied in BiXGBoost to address the over-fitting problem. The results on DREAM4 and Escherichia coli datasets show the good performance of BiXGBoost on different scale of networks. AVAILABILITY AND IMPLEMENTATION: Our Python implementation of BiXGBoost is available at https://github.com/zrq0123/BiXGBoost. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The development of single-cell RNA-sequencing (scRNA-seq) provides a new perspective to study biological problems at the single-cell level. One of the key issues in scRNA-seq analysis is to resolve the heterogeneity and diversity of cells, which is to cluster the cells into several groups. However, many existing clustering methods are designed to analyze bulk RNA-seq data, it is urgent to develop the new scRNA-seq clustering methods. Moreover, the high noise in scRNA-seq data also brings a lot of challenges to computational methods. RESULTS: In this study, we propose a novel scRNA-seq cell type detection method based on similarity learning, called SinNLRR. The method is motivated by the self-expression of the cells with the same group. Specifically, we impose the non-negative and low rank structure on the similarity matrix. We apply alternating direction method of multipliers to solve the optimization problem and propose an adaptive penalty selection method to avoid the sensitivity to the parameters. The learned similarity matrix could be incorporated with spectral clustering, t-distributed stochastic neighbor embedding for visualization and Laplace score for prioritizing gene markers. In contrast to other scRNA-seq clustering methods, our method achieves more robust and accurate results on different datasets. AVAILABILITY AND IMPLEMENTATION: Our MATLAB implementation of SinNLRR is available at, https://github.com/zrq0123/SinNLRR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Chromatin immunoprecipitation sequencing (ChIP-seq) is a technology that combines chromatin immunoprecipitation (ChIP) with next generation of sequencing technology (NGS) to analyze protein interactions with DNA. At present, most ChIP-seq analysis tools adopt the command line, which lacks user-friendly interfaces. Although some web services with graphical interfaces have been developed for ChIP-seq analysis, these sites cannot provide a comprehensive analysis of ChIP-seq from raw data to downstream analysis. RESULTS: In this study, we develop a web service for the whole process of ChIP-Seq Analysis (CSA), which covers mapping, quality control, peak calling, and downstream analysis. In addition, CSA provides a customization function for users to define their own workflows. And the visualization of mapping, peak calling, motif finding, and pathway analysis results are also provided in CSA. For the different types of ChIP-seq datasets, CSA can provide the corresponding tool to perform the analysis. Moreover, CSA can detect differences in ChIP signals between ChIP samples and controls to identify absolute binding sites. CONCLUSIONS: The two case studies demonstrate the effectiveness of CSA, which can complete the whole procedure of ChIP-seq analysis. CSA provides a web interface for users, and implements the visualization of every analysis step. The website of CSA is available at http://CompuBio.csu.edu.cn.

BACKGROUND: A drug-drug interaction (DDI) is defined as a drug effect modified by another drug, which is very common in treating complex diseases such as cancer. Many studies have evidenced that some DDIs could be an increase or a decrease of the drug effect. However, the adverse DDIs maybe result in severe morbidity and even morality of patients, which also cause some drugs to withdraw from the market. As the multi-drug treatment becomes more and more common, identifying the potential DDIs has become the key issue in drug development and disease treatment. However, traditional biological experimental methods, including in vitro and vivo, are very time-consuming and expensive to validate new DDIs. With the development of high-throughput sequencing technology, many pharmaceutical studies and various bioinformatics data provide unprecedented opportunities to study DDIs. RESULT: In this study, we propose a method to predict new DDIs, namely DDIGIP, which is based on Gaussian Interaction Profile (GIP) kernel on the drug-drug interaction profiles and the Regularized Least Squares (RLS) classifier. In addition, we also use the k-nearest neighbors (KNN) to calculate the initial relational score in the presence of new drugs via the chemical, biological, phenotypic data of drugs. We compare the prediction performance of DDIGIP with other competing methods via the 5-fold cross validation, 10-cross validation and de novo drug validation. CONLUSION: In 5-fold cross validation and 10-cross validation, DDRGIP method achieves the area under the ROC curve (AUC) of 0.9600 and 0.9636 which are better than state-of-the-art method (L1 Classifier ensemble method) of 0.9570 and 0.9599. Furthermore, for new drugs, the AUC value of DDIGIP in de novo drug validation reaches 0.9262 which also outperforms the other state-of-the-art method (Weighted average ensemble method) of 0.9073. Case studies and these results demonstrate that DDRGIP is an effective method to predict DDIs while being beneficial to drug development and disease treatment.

BACKGROUND: Essential proteins are crucial for cellular life and thus, identification of essential proteins is an important topic and a challenging problem for researchers. Recently lots of computational approaches have been proposed to handle this problem. However, traditional centrality methods cannot fully represent the topological features of biological networks. In addition, identifying essential proteins is an imbalanced learning problem; but few current shallow machine learning-based methods are designed to handle the imbalanced characteristics. RESULTS: We develop DeepEP based on a deep learning framework that uses the node2vec technique, multi-scale convolutional neural networks and a sampling technique to identify essential proteins. In DeepEP, the node2vec technique is applied to automatically learn topological and semantic features for each protein in protein-protein interaction (PPI) network. Gene expression profiles are treated as images and multi-scale convolutional neural networks are applied to extract their patterns. In addition, DeepEP uses a sampling method to alleviate the imbalanced characteristics. The sampling method samples the same number of the majority and minority samples in a training epoch, which is not biased to any class in training process. The experimental results show that DeepEP outperforms traditional centrality methods. Moreover, DeepEP is better than shallow machine learning-based methods. Detailed analyses show that the dense vectors which are generated by node2vec technique contribute a lot to the improved performance. It is clear that the node2vec technique effectively captures the topological and semantic properties of PPI network. The sampling method also improves the performance of identifying essential proteins. CONCLUSION: We demonstrate that DeepEP improves the prediction performance by integrating multiple deep learning techniques and a sampling method. DeepEP is more effective than existing methods.

Benign epilepsy with centrotemporal spikes (BECT) may be the most popular epilepsy to attack children. In recent years, more and more studies have shown that magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) are promising techniques in distinguishing BECT patients from healthy controls. However, these existing works have suffered from two limitations. On the one hand, they have paid more attention to the brain changes between BETC and healthy controls than developing machine learning methods that can recognize BECT patients. On the other hand, most of the existing approaches extract hand‐crafted features from MRI or fMRI, which cannot obtain the desired performance due to the limited representative capacity of the used features. To address these issues, we propose a novel classification method by fusing the predictions of three different views: hand‐crafted features view, MRI view, and fMRI view. The final result is obtained by passing through those predictions after a fusing neural network. The basic idea of our method is that multiple views could provide complementary information and thus can boost the classification performance. Extensive experiments show that the proposed multi‐view method is remarkably superior to single‐view methods.

Nowadays, for different purposes and contexts, we interact with a lot of different smart devices in our daily lives. Most of these devices are connected to the Internet and are therefore commonly referred to as the Internet of Things (IoT). Mobile edge computing (MEC) has recently evolved as an emerging technique by moving the computing and storage resources from the cloud to the edge of the network. The MEC supports IoT devices to improve their efficiency and scalability; helps to reduce latency delay for real-time applications, bandwidth bottlenecks, and energy consumption; and delivers contextual information processing. MEC offers many features and capabilities, such as access to a multitude of network interface (from 4G and 5G to Wi-Fi), support for device mobility, device context, geo-location awareness, and geographical distribution. Such attributes can support the real-time processing requirements of the Internet of Everything application, such as patient care, disaster management and detection (e.g., earthquakes), and flood monitoring. However, to fully exploit the potential of MEC in the IoT applications, many challenges need to be addressed, such as issues related to IoT Big Data, effective management of data storage and computing, privacy and security concerns, and innovative and emerging communication paradigm (e.g., 5G), require new architectures, applications, and methods.

ICD-9 (the Ninth Revision of International Classification of Diseases) is widely used to describe a patient's diagnosis. Accurate automated ICD-9 coding is important because manual coding is expensive, time-consuming, and inefficient. Inspired by the recent successes of deep learning, in this study, we present a deep learning framework called DeepLabeler to automatically assign ICD-9 codes. DeepLabeler combines the convolutional neural network with the 'Document to Vector' technique to extract and encode local and global features. Our proposed DeepLabeler demonstrates its effectiveness by achieving state-of-the-art performance, i.e., 0.335 micro F-measure on MIMIC-II dataset and 0.408 micro F-measure on MIMIC-III dataset. It outperforms classical hierarchy-based SVM and flat-SVM both on these two datasets by at least 14 percent. Furthermore, we analyze the deep neural network structure to discover the vital elements in the success of DeepLabeler. We find that the convolutional neural network is the most effective component in our network and the 'Document to Vector' technique is also necessary for enhancing classification performance since it extracts well-recognized global features. Extensive experimental results demonstrate that the great promise of deep learning techniques in the field of text multi-label classification and automated medical coding.

Identification of essential proteins based on protein interaction network (PIN) is a very important and hot topic in the post genome era. Up to now, a number of network-based essential protein discovery methods have been proposed. Generally, a static protein interaction network was constructed by using the protein-protein interactions obtained from different experiments or databases. Unfortunately, most of the network-based essential protein discovery methods are sensitive to the reliability of the constructed PIN. In this paper, we propose a new method for constructing refined PIN by using gene expression profiles and subcellular location information. The basic idea behind refining the PIN is that two proteins should have higher possibility to physically interact with each other if they appear together at the same subcellular location and are active together at least at a time point in the cell cycle. The original static PIN is denoted by S-PIN while the final PIN refined by our method is denoted by TS-PIN. To evaluate whether the constructed TS-PIN is more suitable to be used in the identification of essential proteins, 10 network-based essential protein discovery methods (DC, EC, SC, BC, CC, IC, LAC, NC, BN, and DMNC) are applied on it to identify essential proteins. A comparison of TS-PIN and two other networks: S-PIN and NF-APIN (a noise-filtered active PIN constructed by using gene expression data and S-PIN) is implemented on the prediction of essential proteins by using these ten network-based methods. The comparison results show that all of the 10 network-based methods achieve better results when being applied on TS-PIN than that being applied on S-PIN and NF-APIN.

The sequence assembly process can be divided into three stages: contigs extension, scaffolding, and gap filling. The scaffolding method is an essential step during the process to infer the direction and sequence relationships between the contigs. However, scaffolding still faces the challenges of uneven sequencing depth, genome repetitive regions, and sequencing errors, which often leads to many false relationships between contigs. The performance of scaffolding can be improved by removing potential false conjunctions between contigs. In this study, a novel scaffolding algorithm which is on the basis of path extension Loose-Strict-Loose strategy and contig error correction, called iLSLS. iLSLS helps reduce the false relationships between contigs, and improve the accuracy of subsequent steps. iLSLS utilizes a scoring function, which estimates the correctness of candidate paths by the distribution of paired reads, and try to conduction the extension with the path which is scored the highest. What's more, iLSLS can precisely estimate the gap size. We conduct experiments on two real datasets, and the results show that LSLS strategy is efficient to increase the correctness of scaffolds, and iLSLS performs better than other scaffolding methods.

Drug repositioning is an efficient and promising strategy to identify new indications for existing drugs, which can improve the productivity of traditional drug discovery and development. Rapid advances in high-throughput technologies have generated various types of biomedical data over the past decades, which lay the foundations for furthering the development of computational drug repositioning approaches. Although many researches have tried to improve the repositioning accuracy by integrating information from multiple sources and different levels, it is still appealing to further investigate how to efficiently exploit valuable data for drug repositioning. In this study, we propose an efficient approach, Random Walk on a Heterogeneous Network for Drug Repositioning (RWHNDR), to prioritize candidate drugs for diseases. First, an integrated heterogeneous network is constructed by combining multiple sources including drugs, drug targets, diseases and disease genes data. Then, a random walk model is developed to capture the global information of the heterogeneous network. RWHNDR takes advantage of drug targets and disease genes data more comprehensively for drug repositioning. The experiment results show that our approach can achieve better performance, compared with other state-of-the-art approaches which prioritized candidate drugs based on multi-source data.

MicroRNAs (miRNAs) are a class of non-coding RNAs about ∼ 22nt nucleotides. Studies have proven that miRNAs play key roles in many human complex diseases. Therefore, discovering miRNA-disease associations is beneficial to understanding disease mechanisms, developing drugs, and treating complex diseases. It is well known that it is a time-consuming and expensive process to discover the miRNA-disease associations via biological experiments. Alternatively, computational models could provide a low-cost and high-efficiency way for predicting miRNA-disease associations. In this study, we propose a method (called DNRLMF-MDA) to predict miRNA-disease associations based on dynamic neighborhood regularized logistic matrix factorization. DNRLMF-MDA integrates known miRNA-disease associations, functional similarity and Gaussian Interaction Profile (GIP) kernel similarity of miRNAs, and functional similarity and GIP kernel similarity of diseases. Especially, positive observations (known miRNA-disease associations) are assigned higher importance levels than negative observations (unknown miRNA-disease associations).DNRLMF-MDA computes the probability that a miRNA would interact with a disease by a logistic matrix factorization method, where latent vectors of miRNAs and diseases represent the properties of miRNAs and diseases, respectively, and further improve prediction performance via dynamic neighborhood regularized. The 5-fold cross validation is adopted to assess the performance of our DNRLMF-MDA, as well as other competing methods for comparison. The computational experiments show that DNRLMF-MDA outperforms the state-of-art method PBMDA. The AUC values of DNRLMF-MDA on three datasets are 0.9357, 0.9411, and 0.9416, respectively, which are superior to the PBMDA's results of 0.9218, 0.9187, and 0.9262. The average computation times per 5-fold cross validation of DNRLMF-MDA on three datasets are 38, 46, and 50 seconds, which are shorter than the PBMDA's average computation times of 10869, 916, and 8448 seconds, respectively. DNRLMF-MDA also can predict potential diseases for new miRNAs. Furthermore, case studies illustrate that DNRLMF-MDA is an effective method to predict miRNA-disease associations.

Hybrid applications (apps) are becoming more and more popular due to their cross-platform capabilities and high performance. These apps use the JavaScript (JS) bridge communication scheme to interoperate between native code and Web code. Although greatly extending the functionalities of hybrid apps by enabling cross-language invocations and making them more powerful, the bridge communication scheme might also cause some new security issues, e.g., cross-language code injection attacks and privacy leaks. In this paper, we propose BridgeTaint, a bi-directional dynamic taint tracking method that can detect bridge security issues in hybrid apps. BridgeTaint uses a method different from existing ones to track tainted data: it records the taint information of sensitive data when the data are transmitted through the bridge, and uses a cross-language taint mapping method to restore the taint tags of corresponding data. Such a novel design enables BridgeTaint to dynamically track tainted data during the execution of the app and analyze hybrid apps developed using frameworks, which cannot be done with existing solutions based on static code analyses. Based on BridgeTaint, we implement the BridgeInspector tool to detect cross-language privacy leaks and code injection attacks in hybrid apps using JS bridges. A benchmark called BridgeBench is also developed for bridge communication security test. The experimental results on BridgeBench and 1172 apps from Android market demonstrate that BridgeInspector can effectively detect potential privacy leaks and cross-language code injection attacks in hybrid apps using bridge communications.

Transmission is crucial for big data analysis and learning in industrial Internet of Things (IoT) systems. To transmit data with limited energy is a challenge. This paper studies the problem of data transmission in energy harvesting systems with capacitor to supply energy where the energy receiving rate varies over time. The energy receiving rate is slower when the capacitor receives more energy. Based on this characteristic, we study the problem of how to transmit more data when the energy receiving time is not continuous. Given many packets that arrive at different time instances, there is a tradeoff between transmitting the packet right now or saving the energy to transmit the future arriving packets. We formalize two types of problems. The first one is how to minimize the total completion time when there is enough energy to transmit all the packets. The second one is how to transmit as many packets as possible when the energy is not enough to transmit all the packets. For the first problem, we give a 1 + α approximation off line algorithm when all the information of the packets and the energy receiving periods is known in advance, and a max{2, β} competitive ratio online algorithm where the information is not known in advance. For the second problem, we study three cases and give a 6 + [h/(b/R)] approximation off line algorithm for the general situation. We also prove that there does not exit a constant competitive ratio online algorithm.

This article aims to develop a new and robust approach to feature representation. Motivated by the success of Auto-Encoders, we first theoretically analyze and summarize the general properties of all algorithms that are based on traditional Auto-Encoders: (1) The reconstruction error of the input cannot be lower than a lower bound, which can be viewed as a guiding principle for reconstructing the input. Additionally, when the input is corrupted with noises, the reconstruction error of the corrupted input also cannot be lower than a lower bound. (2) The reconstruction of a hidden representation achieving its ideal situation is the necessary condition for the reconstruction of the input to reach the ideal state. (3) Minimizing the Frobenius norm of the Jacobian matrix of the hidden representation has a deficiency and may result in a much worse local optimum value. We believe that minimizing the reconstruction error of the hidden representation is more robust than minimizing the Frobenius norm of the Jacobian matrix of the hidden representation. Based on the above analysis, we propose a new model termedDouble Denoising Auto-Encoders(DDAEs), which uses corruption and reconstruction on both the input and the hidden representation. We demonstrate that the proposed model is highly flexible and extensible and has a potentially better capability to learn invariant and robust feature representations. We also show that our model is more robust than Denoising Auto-Encoders (DAEs) for dealing with noises or inessential features. Furthermore, we detail how to train DDAEs with two different pretraining methods by optimizing the objective function in a combined and separate manner, respectively. Comparative experiments illustrate that the proposed model is significantly better for representation learning than the state-of-the-art models.

This Innovative Practice Work-In-Progress paper presents a collaborative virtual computer lab (CVCL) environment to support collaborative learning in cloud-based virtual computer labs. With advances of cloud computing and virtualization technologies, a new paradigm of virtual computer labs has emerged, where students carry out labs on virtualized resources remotely through the Internet. Virtual computer labs bring advantages, such as anywhere, anytime, on-demand access of specialized software and hardware. However, with current implementations, it also makes it difficult for students to collaborate, due to the fact that students are assigned separated virtual working spaces in a remote-accessing environment and there is a lack of support for sharing and collaboration. To address this issue, we develop a CVCL environment that allows students to reserve virtual computers labs with multiple participants and support remote real-time collaboration among the participants during a lab. The CVCL environment will implement several well-defined collaborative lab models, including shared remote collaboration, virtual study room, and virtual tutoring center. This paper describes the overall architecture and main features of the CVCL environment and shows preliminary results.

The dramatic expansion of link capacity in current data center network causes remarkable challenges to the design of new transport layer protocol, that is, how to converge as fast as possible to help data flow effectively utilize the high bandwidth. Meanwhile, the new protocol should be friendly to the traditional TCP because the non-cooperating applications with old TCP versions are widely existing. Therefore, it is important to achieve a trade-off between the aggressiveness and TCP-friendliness in protocol design. In this paper, we first empirically study why the existing typical data center TCP variants naturally fail to guarantee both fast convergence and TCP friendliness. Then, we design FFC, a transport protocol that makes independent decisions and self-adjustment through retrieving the two-dimensional congestion notification from the RTT and ECN. The results of simulation experiments and real implementations show that the fast convergence of FFC leads to the lower flow completion time compared with DX and DCTCP. Meanwhile, when coexisting with the traditional TCP, FFC also presents a moderate competitiveness, while introducing trivial deployment overhead only at the end hosts.

Summary: The molecular interactions in a cell are varying with time and surrounded environmental cues. The construction and analysis of dynamic molecular networks can elucidate dynamic cellular mechanisms of different biological functions and provide a chance to understand complex diseases at the systems level. Here, we develop DyNetViewer, a Cytoscape application that provides a range of functionalities for the construction, analysis and visualization of dynamic protein-protein interaction networks. The current version of DyNetViewer consists of four different dynamic network construction methods, twelve topological variation analysis methods and four clustering algorithms. Moreover, visualization of different topological variation of nodes and clusters over time enables users to quickly identify the most variations across many network states. Availability and implementation: DyNetViewer is freely available with tutorials at the Cytoscape (3.4+) App Store (http://apps.cytoscape.org/apps/dynetviewer). Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Accumulating evidences indicate that long non-coding RNAs (lncRNAs) play pivotal roles in various biological processes. Mutations and dysregulations of lncRNAs are implicated in miscellaneous human diseases. Predicting lncRNA-disease associations is beneficial to disease diagnosis as well as treatment. Although many computational methods have been developed, precisely identifying lncRNA-disease associations, especially for novel lncRNAs, remains challenging. Results: In this study, we propose a method (named SIMCLDA) for predicting potential lncRNA-disease associations based on inductive matrix completion. We compute Gaussian interaction profile kernel of lncRNAs from known lncRNA-disease interactions and functional similarity of diseases based on disease-gene and gene-gene onotology associations. Then, we extract primary feature vectors from Gaussian interaction profile kernel of lncRNAs and functional similarity of diseases by principal component analysis, respectively. For a new lncRNA, we calculate the interaction profile according to the interaction profiles of its neighbors. At last, we complete the association matrix based on the inductive matrix completion framework using the primary feature vectors from the constructed feature matrices. Computational results show that SIMCLDA can effectively predict lncRNA-disease associations with higher accuracy compared with previous methods. Furthermore, case studies show that SIMCLDA can effectively predict candidate lncRNAs for renal cancer, gastric cancer and prostate cancer. Availability and implementation: https://github.com//bioinfomaticsCSU/SIMCLDA. Supplementary information: Supplementary data are available at Bioinformatics online.

Summary Cloud computing is a powerful infrastructure tool for scientific computing and research and attracts many developers to deploy their products on it. Code migration from local to cloud servers is a significant challenge for all companies and organizations that have many legacy programs. Compared to the de novo development method, the automatic translation is more productive and economical. In this paper, a new Java to Spark (J2S) translator is introduced to achieve the automatic translation from sequential Java code to Spark cloud application, which can translate three types of computing intensive programs. The evaluation experiments demonstrate that these translations significantly improve the source program performance in their domains. It is believed that it is a new step in the automatic code migration era of cloud computing.

MicroRNAs (miRNAs) are a type of non-coding RNAs with about ∼22nt nucleotides. Increasing evidences have shown that miRNAs play critical roles in many human diseases. The identification of human disease-related miRNAs is helpful to explore the underlying pathogenesis of diseases. More and more experimental validated associations between miRNAs and diseases have been reported in the recent studies, which provide useful information for new miRNA-disease association discovery. In this study, we propose a computational framework, KBMF-MDI, to predict the associations between miRNAs and diseases based on their similarities. The sequence and function information of miRNAs are used to measure similarity among miRNAs while the semantic and function information of disease are used to measure similarity among diseases, respectively. In addition, the kernelized Bayesian matrix factorization method is employed to infer potential miRNA-disease associations by integrating these data sources. We applied this method to 6,084 known miRNA-disease associations and utilized 5-fold cross validation to evaluate the performance. The experimental results demonstrate that our method can effectively predict unknown miRNA-disease associations.

Regions of interest (ROIs) based classification has been widely investigated for analysis of brain magnetic resonance imaging (MRI) images to assist the diagnosis of Alzheimer's disease (AD) including its early warning and developing stages, e.g., mild cognitive impairment (MCI) including MCI converted to AD (MCIc) and MCI not converted to AD (MCInc). Since an ROI representation of brain structures is obtained either by pre-definition or by adaptive parcellation, the corresponding ROI in different brains can be measured. However, due to noise and small sample size of MRI images, representations generated from single or multiple ROIs may not be sufficient to reveal the underlying anatomical differences between the groups of disease-affected patients and health controls (HC). In this paper, we employ a whole brain hierarchical network (WBHN) to represent each subject. The whole brain of each subject is divided into 90, 54, 14, and 1 regions based on Automated Anatomical Labeling (AAL) atlas. The connectivity between each pair of regions is computed in terms of Pearson's correlation coefficient and used as classification feature. Then, to reduce the dimensionality of features, we select the features with higher scores. Finally, we use multiple kernel boosting (MKBoost) algorithm to perform the classification. Our proposed method is evaluated on MRI images of 710 subjects (200 AD, 120 MCIc, 160 MCInc, and 230 HC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our proposed method achieves an accuracy of 94.65 percent and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.954 for AD/HC classification, an accuracy of 89.63 percent and an AUC of 0.907 for AD/MCI classification, an accuracy of 85.79 percent and an AUC of 0.826 for MCI/HC classification, and an accuracy of 72.08 percent and an AUC of 0.716 for MCIc/MCInc classification, respectively. Our results demonstrate that our proposed method is efficient and promising for clinical applications for the diagnosis of AD via MRI images.

Several anatomical magnetic resonance imaging (MRI) markers for Alzheimer's disease (AD) have been identified. Cortical gray matter volume, cortical thickness, and subcortical volume have been used successfully to assist the diagnosis of Alzheimer's disease including its early warning and developing stages, e.g., mild cognitive impairment (MCI) including MCI converted to AD (MCIc) and MCI not converted to AD (MCInc). Currently, these anatomical MRI measures have mainly been used separately. Thus, the full potential of anatomical MRI scans for AD diagnosis might not yet have been used optimally. Meanwhile, most studies currently only focused on morphological features of regions of interest (ROIs) or interregional features without considering the combination of them. To further improve the diagnosis of AD, we propose a novel approach of extracting ROI features and interregional features based on multiple measures from MRI images to distinguish AD, MCI (including MCIc and MCInc), and health control (HC). First, we construct six individual networks based on six different anatomical measures (i.e., CGMV, CT, CSA, CC, CFI, and SV) and Automated Anatomical Labeling (AAL) atlas for each subject. Then, for each individual network, we extract all node (ROI) features and edge (interregional) features, and denoted as node feature set and edge feature set, respectively. Therefore, we can obtain six node feature sets and six edge feature sets from six different anatomical measures. Next, each feature within a feature set is ranked by -score in descending order, and the top ranked features of each feature set are applied to MKBoost algorithm to obtain the best classification accuracy. After obtaining the best classification accuracy, we can get the optimal feature subset and the corresponding classifier for each node or edge feature set. Afterwards, to investigate the classification performance with only node features, we proposed a weighted multiple kernel learning (wMKL) framework to combine these six optimal node feature subsets, and obtain a combined classifier to perform AD classification. Similarly, we can obtain the classification performance with only edge features. Finally, we combine both six optimal node feature subsets and six optimal edge feature subsets to further improve the classification performance. Experimental results show that the proposed method outperforms some state-of-the-art methods in AD classification, and demonstrate that different measures contain complementary information.

The de novo assembly aims to reconstruct the genome of the unknown species. Many algorithms have been proposed for de novo assemblies. Due to problems of repetitive regions and sequencing errors, contigs usually contain a large amount of misassemblies. Consequently, the misassembly correction of contigs is a challenging and significant work, which receives considerable attentions from researchers. In this study, we propose a novel method, called MEC, to identify and correct misassemblies in contigs. Firstly, MEC takes fragment coverage as the feature to detect the candidate misassemblies. Then, it can distinguish a large number of false positives from the candidate misassemblies based on the distribution of paired-end reads and the statistical analysis of GC-contents. We apply MEC to four real contig datasets, and carry out experiments to analyze the influence of MEC on scaffolding results, which shows that MEC can reduce misassemblies effectively and result in quantitative improvements in scaffolding quality. MEC is publicly available for download at https://github.com/bioinfomaticsCSU/MEC.

Pairwise sequence alignment is a common and fundamental task in Computational Biology, which constitutes the basis for many Bioinformatics applications. In the post-genomic era, there is an increasing demand to align long DNA sequences to discover their functions. In this paper, we propose a parallel pairwise alignment algorithm for large genomic sequences by recursively dividing the whole genomic sequences into small pieces, with an effective pruning strategy to reduce search and computation space. We implemented rigorous tests on a 4-core computer using real genomic sequences and artificially generated sequences. The results show that our implementation can achieve speedup 10.64 with 99.75% accuracy compared to the sequential algorithm. As far as we know, this is the first time that MBP (mega base-pairs) sequences are globally aligned with an affine gap penalty.

Protein-protein interactions (PPIs) participate in all important biological processes in living organisms, such as catalyzing metabolic reactions, DNA replication, DNA transcription, responding to stimuli and transporting molecules from one location to another. To reveal the function mechanisms in cells, it is important to identify PPIs that take place in the living organism. A large number of PPIs have been discovered by high-throughput experiments and computational methods. However, false-positive PPIs have been introduced too. Therefore, to obtain reliable PPIs, many computational methods have been proposed. Generally, these methods can be classified into two categories. One category includes the methods that are designed to determine new reliable PPIs. The other one is designed to assess the reliability of existing PPIs and filter out the unreliable ones. In this article, we review the two kinds of methods for detecting reliable PPIs, and then focus on evaluating the performance of some of these typical methods. Later on, we also enumerate several PPI network-based applications with taking a reliability assessment of the PPI data into consideration. Finally, we will discuss the challenges for obtaining reliable PPIs and future directions of the construction of reliable PPI networks. Our research will provide readers some guidance for choosing appropriate methods and features for obtaining reliable PPIs.

Motivation: Increasing evidences have demonstrated that long noncoding RNAs (lncRNAs) play important roles in many human diseases. Therefore, predicting novel lncRNA-disease associations would contribute to dissect the complex mechanisms of disease pathogenesis. Some computational methods have been developed to infer lncRNA-disease associations. However, most of these methods infer lncRNA-disease associations only based on single data resource. Results: In this paper, we propose a new computational method to predict lncRNA-disease associations by integrating multiple biological data resources. Then, we implement this method as a web server for lncRNA-disease association prediction (LDAP). The input of the LDAP server is the lncRNA sequence. The LDAP predicts potential lncRNA-disease associations by using a bagging SVM classifier based on lncRNA similarity and disease similarity. Availability and Implementation: The web server is available at http://bioinformatics.csu.edu.cn/ldap Contact: [email protected]. Supplimentary Information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Methylation is a common modification of DNA. It has been a very important and hot topic to study the correlation between methylation and diseases in medical science. Because of the special process with bisulfite treatment, traditional mapping tools do not work well with such methylation experimental reads. Traditional aligners are not designed for mapping bisulfite-treated reads, where the un-methylated 'C's are converted to 'T's. RESULTS: In this paper, we develop a reliable and visual tool, named VAliBS, for mapping bisulfate sequences to a genome reference. VAliBS works well even on large scale data or high noise data. By comparing with other state-of-the-art tools (BisMark, BSMAP, BS-Seeker2), VAliBS can improve the accuracy of bisulfite mapping. Moreover, VAliBS is a visual tool which makes its operations more easily and the alignment results are shown with colored marks which makes it easier to be read. VAliBS provides fast and accurate mapping of bisulfite-converted reads, and a friendly window system to visualize the detail of mapping of each read. CONCLUSIONS: VAliBS works well on both simulated data and real data. It can be useful in DNA methylation research. VALiBS implements an X-Window user interface where the methylation positions are visual and the operations are friendly.

BACKGROUND: RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Meantime, knowledge-based technologies are experiencing a potential revolution ignited by the new deep learning methods. By scanning the newly found data set from RNA-seq, scientists have found that: (1) the expression of lincRNAs appears to be regulated, that is, the relevance exists along the DNA sequences; (2) lincRNAs contain some conversed patterns/motifs tethered together by non-conserved regions. The two evidences give the reasoning for adopting knowledge-based deep learning methods in lincRNA detection. Similar to coding region transcription, non-coding regions are split at transcriptional sites. However, regulatory RNAs rather than message RNAs are generated. That is, the transcribed RNAs participate the biological process as regulatory units instead of generating proteins. Identifying these transcriptional regions from non-coding regions is the first step towards lincRNA recognition. RESULTS: The auto-encoder method achieves 100% and 92.4% prediction accuracy on transcription sites over the putative data sets. The experimental results also show the excellent performance of predictive deep neural network on the lincRNA data sets compared with support vector machine and traditional neural network. In addition, it is validated through the newly discovered lincRNA data set and one unreported transcription site is found by feeding the whole annotated sequences through the deep learning machine, which indicates that deep learning method has the extensive ability for lincRNA prediction. CONCLUSIONS: The transcriptional sequences of lincRNAs are collected from the annotated human DNA genome data. Subsequently, a two-layer deep neural network is developed for the lincRNA detection, which adopts the auto-encoder algorithm and utilizes different encoding schemes to obtain the best performance over intergenic DNA sequence data. Driven by those newly annotated lincRNA data, deep learning methods based on auto-encoder algorithm can exert their capability in knowledge learning in order to capture the useful features and the information correlation along DNA genome sequences for lincRNA detection. As our knowledge, this is the first application to adopt the deep learning techniques for identifying lincRNA transcription sequences.

The papers in this special section were presented at the 10th International Symposium on Bioinformatics Research and Applications (ISBRA 2014), which was held at Zhangjiajie, China, June 28-30, 2014.

Taking the advantage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unraveling complex relationships between genotypes and phenotypes. Current multi-locus-based methods are insufficient to detect interactions with diverse genetic effects on multifarious diseases. Also, statistic tests for high-order epistasis ( ≥ 2 SNPs) raise huge computational and analytical challenges because the computation increases exponentially as the growth of the cardinality of SNPs combinations. In this paper, we provide a simple, fast and powerful method, named DAM, using Bayesian inference to detect genome-wide multi-locus epistatic interactions in multiple diseases. Experimental results on simulated data demonstrate that our method is powerful and efficient. We also apply DAM on two GWAS datasets from WTCCC, i.e., Rheumatoid Arthritis and Type 1 Diabetes, and identify some novel findings. Therefore, we believe that our method is suitable and efficient for the full-scale analysis of multi-disease-related interactions in GWASs.

The purpose of de novo assembly is to report more contiguous, complete, and less error prone contigs. Thanks to the advent of the next generation sequencing (NGS) technologies, the cost of producing high depth reads is reduced greatly. However, due to the disadvantages of NGS, de novo assembly has to face the difficulties brought by repeat regions, error rate, and low sequencing coverage in some regions. Although many de novo algorithms have been proposed to solve these problems, the de novo assembly still remains a challenge. In this article, we developed an iterative seed-extension algorithm for de novo assembly, called ISEA. To avoid the negative impact induced by error rate, ISEA utilizes reads overlap and paired-end information to correct error reads before assemblying. During extending seeds in a De Bruijn graph, ISEA uses an elaborately designed score function based on paired-end information and the distribution of insert size to solve the repeat region problem. By employing the distribution of insert size, the score function can also reduce the influence of error reads. In scaffolding, ISEA adopts a relaxed strategy to join contigs that were terminated for low coverage during the extension. The performance of ISEA was compared with six previous popular assemblers on four real datasets. The experimental results demonstrate that ISEA can effectively obtain longer and more accurate scaffolds.

Since proteins are digested into a mixture of peptides in the preprocessing step of tandem mass spectrometry (MS), it is difficult to determine which specific protein a shared peptide belongs to. In recent studies, besides tandem MS data and peptide identification information, some other information is exploited to infer proteins. Different from the methods which first use only tandem MS data to infer proteins and then use network information to refine them, this study proposes a protein inference method named TMSIN, which uses interactome networks directly. As two interacting proteins should co-exist, it is reasonable to assume that if one of the interacting proteins is confidently inferred in a sample, its interacting partners should have a high probability in the same sample, too. Therefore, we can use the neighborhood information of a protein in an interactome network to adjust the probability that the shared peptide belongs to the protein. In TMSIN, a multi-weighted graph is constructed by incorporating the bipartite graph with interactome network information, where the bipartite graph is built with the peptide identification information. Based on multi-weighted graphs, TMSIN adopts an iterative workflow to infer proteins. At each iterative step, the probability that a shared peptide belongs to a specific protein is calculated by using the Bayes' law based on the neighbor protein support scores of each protein which are mapped by the shared peptides. We carried out experiments on yeast data and human data to evaluate the performance of TMSIN in terms of ROC, q-value, and accuracy. The experimental results show that AUC scores yielded by TMSIN are 0.742 and 0.874 in yeast dataset and human dataset, respectively, and TMSIN yields the maximum number of true positives when q-value less than or equal to 0.05. The overlap analysis shows that TMSIN is an effective complementary approach for protein inference.

Modern data centers host diverse hyper text transfer protocol (HTTP)-based services, which employ persistent transmission control protocol (TCP) connections to send HTTP requests and responses. However, the ON/OFF pattern of HTTP traffic disturbs the increase of TCP congestion window, potentially triggering packet loss at the beginning of ON period. Furthermore, the transmission performance becomes worse due to severe congestion in the concurrent transfer of HTTP response. In this paper, we provide the first extensive study to investigate the root cause of performance degradation of highly concurrent HTTP connections in data center network. We further present the design and implementation of TCP-TRIM, which employs probe packets to smooth the aggressive increase of congestion window in persistent TCP connection and leverages congestion detection and control at end-host to limit the growth of switch queue length under highly concurrent TCP connections. The experimental results of at-scale simulations and real implementations demonstrate that TCP-TRIM reduces the completion time of HTTP response by up to 80%, while introducing little deployment overhead only at the end hosts.

Increasing evidences have shown that microRNAs (miRNAs) play important roles in many diseases. The environmental factors (EFs) can regulate the expression level of miRNAs in human tissues. Therefore, identifying potential miRNA-environmental factor interactions is helpful not only for understanding the pathogenesis of diseases, but also for disease diagnosis, prognosis and treatment. In this paper, we propose a computational framework, MEI-BRWMLL (MiRNA-EF Interaction prediction based on Bi-Random walk and Multi-Label Learning), to identify interactions between miRNAs and environmental factors. The sequence and topology information of miRNA and structure, anatomical therapeutic chemical and topology information of environmental factor are employed to measure similarity of miRNAs and environmental factors, respectively. In addition, we use similarity network fusion method to integrate biological information of miRNAs and environmental factors, respectively. In the last, the bi-random walk and multi-label learning method are utilized to identify potential miRNA-environmental factor interactions. In order to evaluate the performance of MEI-BRWMLL, we implement the ten-fold cross validation in the experiment. The MEI-BRWMLL achieves an AUC of 0.8208. It has been shown that MEI-BRWMLL is able to identify known miRNA-environmental factor interactions.

With the advances in high-throughput technology, a large number of protein interactions data have been burgeoning in recent years, which makes it possible for considering dynamic properties of protein interaction networks(PINs) instead of static properties. To address the limitation of the existing dynamic PIN analysis approaches, in this paper, we proposed a new model-based scheme for the construction of the Spatial and Temporal Active Protein Interaction Network (ST-APIN) by integrating time-course gene expression data and subcellular location information. To evaluate the efficiency of ST-APIN, the commonly used classical clustering algorithm MCL was adopted to identify protein complexes from ST-APIN and other three dynamic PINs, NF-APIN, DPIN, TC-PIN. The experimental results showed that, the performance of MCL on ST-APIN outperforms those on the three other dynamic networks in terms of matching with known complexes, sensitivity, specificity and f-measure. Furthermore, we evaluated the identified protein complexes by GO (Gene Ontology) function enrichment analysis. The validation showed that the identified protein complexes from ST-APIN were more biologically significance. This study provided a general paradigm for constructing the ST-APINs, which can be used for theoretical studies and clinic applications.

MicroRNA(miRNA) plays an important role in regulating the expression of target mRNAs. The deregulation of microRNAs appears to associate with various diseases. Recently, researchers focus on making use of various biological properties to identify the associations between microRNAs and diseases so as to provide helpful information for disease therapies. Accumulate evidences have shown that the inter- and intra-relationships of microRNAs, diseases, environment factors and genes contribute to correctly detect candidate microRNA-disease associations. However, there lack of methods that can comprehensively make use of the advantage of these relationships. In this work, we construct four separate biological networks, that are microRNA functional similarity network(MFN), disease semantic similarity network(DSN), environmental factor chemical structure similarity network(ESN) and gene-gene functional similarity network( GSN). After that, an unbalanced four random walking method, namely FourRW is implemented on the four networks, which not only can flexibly infer information from different levels of neighbors in the four networks, but also realizes the information transfer between different networks. The results of experiment show that our method achieves better prediction performance than the other state-of-the-art methods.

Modern data centers host diverse HTTP-based services, which employ persistent TCP connections to send HTTP requests and responses. However, the ON/OFF pattern of HTTP traffic disturbs the increase of TCP congestion window, potentially triggering packet loss at the beginning of ON period. Furthermore, the transmission performance becomes worse due to severe congestion in the concurrent transfer of HTTP response. In this work, we first reveal that the TCP's aggressive behavior in increasing congestion window causes TCP timeouts and throughput collapse. We further present the design and implementation of TCP-TRIM, which employs probe packets to smooth the aggressive increase of congestion window in persistent TCP connection, and leverages congestion detection and control at end-host to limit the growth of switch queue length under highly concurrent TCP connections. The experimental results of at-scale simulations and real implementations show that TCPTRIM reduces the completion time of HTTP response by up to 80%, while introducing little deployment overhead only at the end hosts.

This poster documents three approaches that we are undertaking to increase security awareness within undergraduate computer science classes. The first approach is a verbal password entry system, with surreptitious photos being taken when the mobile device is stolen. The second approach is a lab where students develop a password entry and verification system between a mobile device and a remote server. The third approach is a captcha system, where students implement a simple challenge that can be verified. Like password entry, the captcha communications must be secure and difficult to automatically manipulate. Unlike password entry, the captcha is meant to allow humans access while denying other computers.

MOTIVATION: Drug repositioning, which aims to identify new indications for existing drugs, offers a promising alternative to reduce the total time and cost of traditional drug development. Many computational strategies for drug repositioning have been proposed, which are based on similarities among drugs and diseases. Current studies typically use either only drug-related properties (e.g. chemical structures) or only disease-related properties (e.g. phenotypes) to calculate drug or disease similarity, respectively, while not taking into account the influence of known drug-disease association information on the similarity measures. RESULTS: In this article, based on the assumption that similar drugs are normally associated with similar diseases and vice versa, we propose a novel computational method named MBiRW, which utilizes some comprehensive similarity measures and Bi-Random walk (BiRW) algorithm to identify potential novel indications for a given drug. By integrating drug or disease features information with known drug-disease associations, the comprehensive similarity measures are firstly developed to calculate similarity for drugs and diseases. Then drug similarity network and disease similarity network are constructed, and they are incorporated into a heterogeneous network with known drug-disease interactions. Based on the drug-disease heterogeneous network, BiRW algorithm is adopted to predict novel potential drug-disease associations. Computational experiment results from various datasets demonstrate that the proposed approach has reliable prediction performance and outperforms several recent computational drug repositioning approaches. Moreover, case studies of five selected drugs further confirm the superior performance of our method to discover potential indications for drugs practically. AVAILABILITY AND IMPLEMENTATION: http://github.com//bioinfomaticsCSU/MBiRW CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Advances of next generation sequencing technologies and availability of short read data enable the detection of structural variations (SVs). Deletions, an important type of SVs, have been suggested in association with genetic diseases. There are three types of deletions: blunt deletions, deletions with microhomologies and deletions with microsinsertions. The last two types are very common in the human genome, but they pose difficulty for the detection. Furthermore, finding deletions from sequencing data remains challenging. It is highly appealing to develop sensitive and accurate methods to detect deletions from sequencing data, especially deletions with microhomology and deletions with microinsertion. RESULTS: We present a novel method called Sprites (SPlit Read re-alIgnment To dEtect Structural variants) which finds deletions from sequencing data. It aligns a whole soft-clipping read rather than its clipped part to the target sequence, a segment of the reference which is determined by spanning reads, in order to find the longest prefix or suffix of the read that has a match in the target sequence. This alignment aims to solve the problem of deletions with microhomologies and deletions with microinsertions. Using both simulated and real data we show that Sprites performs better on detecting deletions compared with other current methods in terms of F-score. AVAILABILITY AND IMPLEMENTATION: Sprites is open source software and freely available at https://github.com/zhangzhen/sprites CONTACT: [email protected] data: Supplementary data are available at Bioinformatics online.

BACKGROUND: Essential proteins play an indispensable role in the cellular survival and development. There have been a series of biological experimental methods for finding essential proteins; however they are time-consuming, expensive and inefficient. In order to overcome the shortcomings of biological experimental methods, many computational methods have been proposed to predict essential proteins. The computational methods can be roughly divided into two categories, the topology-based methods and the sequence-based ones. The former use the topological features of protein-protein interaction (PPI) networks while the latter use the sequence features of proteins to predict essential proteins. Nevertheless, it is still challenging to improve the prediction accuracy of the computational methods. RESULTS: Comparing with nonessential proteins, essential proteins appear more frequently in certain subcellular locations and their evolution more conservative. By integrating the information of subcellular localization, orthologous proteins and PPI networks, we propose a novel essential protein prediction method, named SON, in this study. The experimental results on S.cerevisiae data show that the prediction accuracy of SON clearly exceeds that of nine competing methods: DC, BC, IC, CC, SC, EC, NC, PeC and ION. CONCLUSIONS: We demonstrate that, by integrating the information of subcellular localization, orthologous proteins with PPI networks, the accuracy of predicting essential proteins can be improved. Our proposed method SON is effective for predicting essential proteins.

This paper presents a modified ant colony optimization (ACO) approach for the network coding resource minimization problem. It is featured with several attractive mechanisms specially devised for solving the concerned problem: 1) a multidimensional pheromone maintenance mechanism is put forward to address the issue of pheromone overlapping; 2) problem-specific heuristic information is employed to enhance the capability of heuristic search (neighboring area search); 3) a tabu-table-based path construction method is devised to facilitate the construction of feasible (link-disjoint) paths from the source to each receiver; 4) a local pheromone updating rule is developed to guide ants to construct appropriate promising paths; and 5) a solution reconstruction method is presented, with the aim of avoiding prematurity and improving the global search efficiency of proposed algorithm. Due to the way it works, the ACO can well exploit the global and local information of routing-related problems during the solution construction phase. The simulation results on benchmark instances demonstrate that with the integrated five extended mechanisms, our algorithm outperforms a number of existing algorithms with respect to the best solutions obtained and the computational time.

MicroRNAs (miRNAs) are a set of small non-coding RNAs that play critical roles in many human diseases. Identifying potential miRNA-disease association is helpful to explore the underlying molecular mechanisms of disease. Currently, it is expensive and time-consuming to detect miRNA-disease associations with experimental methods. On the other hand, many known associations between miRNAs and diseases provide useful information for new miRNA-disease interaction discovery. In this study, we propose a computational framework to infer the relationship between miRNA and disease by integrating multiple data resources. We use sequence and function information of miRNA and semantic and function information of disease to measure similarity of miRNA and disease, respectively. In addition, kernelized Bayesian matrix factorization method is employed to infer potential miRNA-disease association by integrating these data resources. The experimental results demonstrate that our method can effectively predict unknown miRNA-disease association.

Essential proteins are indispensable to maintain life activities in living organisms, and play important roles in the studies of pathology, synthetic biology, and drug design. Many computational methods are employed to identify essential proteins from Protein-protein Interaction Networks (PINs). In this paper, considering the different importance of protein-protein interactions which take place in different subcellular compartments, a Compartment Importance Centrality (CIC) method is proposed to detect essential proteins by integrating protein subcellular localization information. The experiments were carried on four species (Saccharomyces cerevisiae, Homo sapiens, Mus musculus and Drosophila melanogaster), and the performance of CIC was compared with other centrality methods, including the centrality methods solely based on topology and the ones combining both topology and other biological knowledge. The results show that CIC method has better performance to predict essential protein on four species. Furthermore, different from methods which overfits with the features of essential proteins of one species and may perform poor for other species, CIC has a wide applicable scope to identify essential proteins for different species.

In classification tasks, ensemble selection methods select some base learners from the learners pool instead all of them to classify a query patterns. Static ensemble selection schemes determine the final ensemble immediately after training and apply it to all test patterns. On the other hand, dynamic ensemble selection (DES) construct a customized ensemble for every query pattern by incorporating its local information. Most DES differ each other only on the selection scheme. We propose Dynamic Ensemble Selection with Local Expertise Consistency (DES-LEC) that focus on generating a learners pool dedicated to the latter selection phase. Experiment results on 4 medical data sets suggest that DES-LEC is able to improve the performance over the DES systems that select from a regular learners pool.

MOTIVATION: In genome assembly, as coverage of sequencing and genome size growing, most current softwares require a large memory for handling a great deal of sequence data. However, most researchers usually cannot meet the requirements of computing resources which prevent most current softwares from practical applications. RESULTS: In this article, we present an update algorithm called EPGA2, which applies some new modules and can bring about improved assembly results in small memory. For reducing peak memory in genome assembly, EPGA2 adopts memory-efficient DSK to count K-mers and revised BCALM to construct De Bruijn Graph. Moreover, EPGA2 parallels the step of Contigs Merging and adds Errors Correction in its pipeline. Our experiments demonstrate that all these changes in EPGA2 are more useful for genome assembly. AVAILABILITY AND IMPLEMENTATION: EPGA2 is publicly available for download at https://github.com/bioinfomaticsCSU/EPGA2.

MOTIVATION: In genome assembly, the primary issue is how to determine upstream and downstream sequence regions of sequence seeds for constructing long contigs or scaffolds. When extending one sequence seed, repetitive regions in the genome always cause multiple feasible extension candidates which increase the difficulty of genome assembly. The universally accepted solution is choosing one based on read overlaps and paired-end (mate-pair) reads. However, this solution faces difficulties with regard to some complex repetitive regions. In addition, sequencing errors may produce false repetitive regions and uneven sequencing depth leads some sequence regions to have too few or too many reads. All the aforementioned problems prohibit existing assemblers from getting satisfactory assembly results. RESULTS: In this article, we develop an algorithm, called extract paths for genome assembly (EPGA), which extracts paths from De Bruijn graph for genome assembly. EPGA uses a new score function to evaluate extension candidates based on the distributions of reads and insert size. The distribution of reads can solve problems caused by sequencing errors and short repetitive regions. Through assessing the variation of the distribution of insert size, EPGA can solve problems introduced by some complex repetitive regions. For solving uneven sequencing depth, EPGA uses relative mapping to evaluate extension candidates. On real datasets, we compare the performance of EPGA and other popular assemblers. The experimental results demonstrate that EPGA can effectively obtain longer and more accurate contigs and scaffolds.

MOTIVATION: Based on the next generation genome sequencing technologies, a variety of biological applications are developed, while alignment is the first step once the sequencing reads are obtained. In recent years, many software tools have been developed to efficiently and accurately align short reads to the reference genome. However, there are still many reads that can't be mapped to the reference genome, due to the exceeding of allowable mismatches. Moreover, besides the unmapped reads, the reads with low mapping qualities are also excluded from the downstream analysis, such as variance calling. If we can take advantages of the confident segments of these reads, not only can the alignment rates be improved, but also more information will be provided for the downstream analysis. RESULTS: This paper proposes a method, called RAUR (Re-align the Unmapped Reads), to re-align the reads that can not be mapped by alignment tools. Firstly, it takes advantages of the base quality scores (reported by the sequencer) to figure out the most confident and informative segments of the unmapped reads by controlling the number of possible mismatches in the alignment. Then, combined with an alignment tool, RAUR re-align these segments of the reads. We run RAUR on both simulated data and real data with different read lengths. The results show that many reads which fail to be aligned by the most popular alignment tools (BWA and Bowtie2) can be correctly re-aligned by RAUR, with a similar Precision. Even compared with the BWA-MEM and the local mode of Bowtie2, which perform local alignment for long reads to improve the alignment rate, RAUR also shows advantages on the Alignment rate and Precision in some cases. Therefore, the trimming strategy used in RAUR is useful to improve the Alignment rate of alignment tools for the next-generation genome sequencing. AVAILABILITY: All source code are available at http://netlab.csu.edu.cn/bioinformatics/RAUR.html.

Providing deadline-sensitive services is a challenge in data centers. Because of the conservativeness in additive increase congestion avoidance, current transmission control protocols are inefficient in utilizing the super high bandwidth of data centers. This may cause many deadline-sensitive flows to miss their deadlines before achieving their available bandwidths. We propose an Adaptive-Acceleration Data Center TCP, A2DTCP, which takes into account both network congestion and latency requirement of application service. By using congestion avoidance with an adaptive increase rate that varies between additive and multiplicative, A2DTCP accelerates bandwidth detection thus achieving high bandwidth utilization efficiency. At-scale simulations and real testbed implementations show that A2DTCP significantly reduces the missed deadline ratio compared to D2TCP and DCTCP. In addition, A2DTCP can co-exist with conventional TCP as well without requiring more changes in switch hardware than D2TCP and DCTCP.

Single nucleotide polymorphisms, a dominant type of genetic variants, have been used successfully to identify defective genes causing human single gene diseases. However, most common human diseases are complex diseases and caused by gene-gene and gene-environment interactions. Many SNP-SNP interaction analysis methods have been introduced but they are not powerful enough to discover interactions more than three SNPs. The paper proposes a novel method that analyzes all SNPs simultaneously. Different from existing methods, the method regards an individual's genotype data on a list of SNPs as a point with a unit of energy in a multi-dimensional space, and tries to find a new coordinate system where the energy distribution difference between cases and controls reaches the maximum. The method will find different multiple SNPs combinatorial patterns between cases and controls based on the new coordinate system. The experiment on simulated data shows that the method is efficient. The tests on the real data of age-related macular degeneration (AMD) disease show that it can find out more significant multi-SNP combinatorial patterns than existing methods.

Essential proteins are indispensable for cellular life. It is of great significance to identify essential proteins that can help us understand the minimal requirements for cellular life and is also very important for drug design. However, identification of essential proteins based on experimental approaches are typically time-consuming and expensive. With the development of high-throughput technology in the post-genomic era, more and more protein-protein interaction data can be obtained, which make it possible to study essential proteins from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. Most of these topology based essential protein discovery methods were to use network centralities. In this paper, we investigate the essential proteins' topological characters from a completely new perspective. To our knowledge it is the first time that topology potential is used to identify essential proteins from a protein-protein interaction (PPI) network. The basic idea is that each protein in the network can be viewed as a material particle which creates a potential field around itself and the interaction of all proteins forms a topological field over the network. By defining and computing the value of each protein's topology potential, we can obtain a more precise ranking which reflects the importance of proteins from the PPI network. The experimental results show that topology potential-based methods TP and TP-NC outperform traditional topology measures: degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), subgraph centrality (SC), eigenvector centrality (EC), information centrality (IC), and network centrality (NC) for predicting essential proteins. In addition, these centrality measures are improved on their performance for identifying essential proteins in biological network when controlled by topology potential.

Prediction of essential proteins which are crucial to an organism's survival is important for disease analysis and drug design, as well as the understanding of cellular life. The majority of prediction methods infer the possibility of proteins to be essential by using the network topology. However, these methods are limited to the completeness of available protein-protein interaction (PPI) data and depend on the network accuracy. To overcome these limitations, some computational methods have been proposed. However, seldom of them solve this problem by taking consideration of protein domains. In this work, we first analyze the correlation between the essentiality of proteins and their domain features based on data of 13 species. We find that the proteins containing more protein domain types which rarely occur in other proteins tend to be essential. Accordingly, we propose a new prediction method, named UDoNC, by combining the domain features of proteins with their topological properties in PPI network. In UDoNC, the essentiality of proteins is decided by the number and the frequency of their protein domain types, as well as the essentiality of their adjacent edges measured by edge clustering coefficient. The experimental results on S. cerevisiae data show that UDoNC outperforms other existing methods in terms of area under the curve (AUC). Additionally, UDoNC can also perform well in predicting essential proteins on data of E. coli.

Cluster analysis of biological networks is one of the most important approaches for identifying functional modules and predicting protein functions. Furthermore, visualization of clustering results is crucial to uncover the structure of biological networks. In this paper, ClusterViz, an APP of Cytoscape 3 for cluster analysis and visualization, has been developed. In order to reduce complexity and enable extendibility for ClusterViz, we designed the architecture of ClusterViz based on the framework of Open Services Gateway Initiative. According to the architecture, the implementation of ClusterViz is partitioned into three modules including interface of ClusterViz, clustering algorithms and visualization and export. ClusterViz fascinates the comparison of the results of different algorithms to do further related analysis. Three commonly used clustering algorithms, FAG-EC, EAGLE and MCODE, are included in the current version. Due to adopting the abstract interface of algorithms in module of the clustering algorithms, more clustering algorithms can be included for the future use. To illustrate usability of ClusterViz, we provided three examples with detailed steps from the important scientific articles, which show that our tool has helped several research teams do their research work on the mechanism of the biological networks.

As the volume of data grows at an unprecedented rate, large-scale data mining and knowledge discovery present a tremendous challenge. Rough set theory, which has been used successfully in solving problems in pattern recognition, machine learning, and data mining, centers around the idea that a set of distinct objects may be approximated via a lower and upper bound. In order to obtain the benefits that rough sets can provide for data mining and related tasks, efficient computation of these approximations is vital. The recently introduced cloud computing model, MapReduce, has gained a lot of attention from the scientific community for its applicability to large-scale data analysis. In previous research, we proposed a MapReduce-based method for computing approximations in parallel, which can efficiently process complete data but fails in the case of missing (incomplete) data. To address this shortcoming, three different parallel matrix-based methods are introduced to process large-scale, incomplete data. All of them are built on MapReduce and implemented on Twister that is a lightweight MapReduce runtime system. The proposed parallel methods are then experimentally shown to be efficient for processing large-scale data.

BACKGROUND: Taking the advantage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger. RESULTS: In this paper, we provide a simple, fast and powerful method using dynamic clustering and cloud computing to detect genome-wide multi-locus epistatic interactions. We have constructed systematic experiments to compare powers performance against some recently proposed algorithms, including TEAM, SNPRuler, EDCF and BOOST. Furthermore, we have applied our method on two real GWAS datasets, Age-related macular degeneration (AMD) and Rheumatoid arthritis (RA) datasets, where we find some novel potential disease-related genetic factors which are not shown up in detections of 2-loci epistatic interactions. CONCLUSIONS: Experimental results on simulated data demonstrate that our method is more powerful than some recently proposed methods on both two- and three-locus disease models. Our method has discovered many novel high-order associations that are significantly enriched in cases from two real GWAS datasets. Moreover, the running time of the cloud implementation for our method on AMD dataset and RA dataset are roughly 2 hours and 50 hours on a cluster with forty small virtual machines for detecting two-locus interactions, respectively. Therefore, we believe that our method is suitable and effective for the full-scale analysis of multiple-locus epistatic interactions in GWAS.

Computing systems and networks become increasingly large and complex with a variety of compromises and vulnerabilities. The network security and privacy are of great concern today, where self-defense against different kinds of attacks in an autonomous and holistic manner is a challenging topic. To address this problem, we developed an innovative technology called Bionic Autonomic Nervous System (BANS). The BANS is analogous to biological nervous system, which consists of basic modules like cyber axon, cyber neuron, peripheral nerve and central nerve. We also presented an innovative self-defense mechanism which utilizes the Fuzzy Logic, Neural Networks, and Entropy Awareness, etc. Equipped with the BANS, computer and network systems can intelligently self-defend against both known and unknown compromises/attacks including denial of services (DoS), spyware, malware, and virus. BANS also enabled multiple computers to collaboratively fight against some distributed intelligent attacks like DDoS. We have implemented the BANS in practice. Some case studies and experimental results exhibited the effectiveness and efficiency of the BANS and the self-defense mechanism.

Essential proteins are vital for an organism's viability under a variety of conditions. There are many experimental and computational methods developed to identify essential proteins. Computational prediction of essential proteins based on the global protein-protein interaction (PPI) network is severely restricted because of the insufficiency of the PPI data, but fortunately the gene expression profiles help to make up the deficiency. In this work, Pearson correlation coefficient (PCC) is used to bridge the gap between PPI and gene expression data. Based on PCC and edge clustering coefficient (ECC), a new centrality measure, i.e., the weighted degree centrality (WDC), is developed to achieve the reliable prediction of essential proteins. WDC is employed to identify essential proteins in the yeast PPI and e-Coli networks in order to estimate its performance. For comparison, other prediction technologies are also performed to identify essential proteins. Some evaluation methods are used to analyze the results from various prediction approaches. The prediction results and comparative analyses are shown in the paper. Furthermore, the parameter λ in the method WDC will be analyzed in detail and an optimal λ value will be found. Based on the optimal λ value, the differentiation of WDC and another prediction method PeC is discussed. The analyses prove that WDC outperforms other methods including DC, BC, CC, SC, EC, IC, NC, and PeC. At the same time, the analyses also mean that it is an effective way to predict essential proteins by means of integrating different data sources.

Advanced biological technologies are producing large-scale protein-protein interaction (PPI) data at an ever increasing pace, which enable us to identify protein complexes from PPI networks. Pair-wise protein interactions can be modeled as a graph, where vertices represent proteins and edges represent PPIs. However most of current algorithms detect protein complexes based on deterministic graphs, whose edges are either present or absent. Neighboring information is neglected in these methods. Based on the uncertain graph model, we propose the concept of expected density to assess the density degree of a subgraph, the concept of relative degree to describe the relationship between a protein and a subgraph in a PPI network. We develop an algorithm called DCU (detecting complex based on uncertain graph model) to detect complexes from PPI networks. In our method, the expected density combined with the relative degree is used to determine whether a subgraph represents a complex with high cohesion and low coupling. We apply our method and the existing competing algorithms to two yeast PPI networks. Experimental results indicate that our method performs significantly better than the state-of-the-art methods and the proposed model can provide more insights for future study in PPI networks.

Data Gathering is a fundamental task in Wireless Sensor Networks (WSNs). Data gathering trees capable of performing aggregation operations are also referred to as Data Aggregation Trees (DATs). Currently, most of the existing works focus on constructing DATs according to different user requirements under the Deterministic Network Model (DNM). However, due to the existence of many probabilistic lossy links in WSNs, it is more practical to obtain a DAT under the realistic Probabilistic Network Model (PNM). Moreover, the load-balance factor is neglected when constructing DATs in current literatures. Therefore, in this paper, we focus on constructing a Load-Balanced Data Aggregation Tree (LBDAT) under the PNM. More specifically, three problems are investigated, namely, the Load-Balanced Maximal Independent Set (LBMIS) problem, the Connected Maximal Independent Set (CMIS) problem, and the LBDAT construction problem. LBMIS and CMIS are well-known NP-hard problems and LBDAT is an NP-complete problem. Consequently, approximation algorithms and comprehensive theoretical analysis of the approximation factors are presented in the paper. Finally, our simulation results show that the proposed algorithms outperform the existing state-of-the-art approaches significantly.

ABSTRACT Inspired by the backbone concept in wired networks, a virtual backbone is expected to bring substantial benefits to routing in wireless sensor networks (WSNs). A connected dominating set (CDS) is used as a virtual backbone for efficient routing and broadcasting in WSNs. Most existing works focus on constructing a minimum CDS, ak‐connectm‐dominating CDS, a minimum routing cost CDS, or a bounded‐diameter CDS. However, theload‐balancefactor is not considered for CDSs in WSNs. In this paper, a greedy‐based approximation algorithm is proposed to construct load‐balanced CDS in a WSN. More importantly, we propose a new problem: the Load‐balanced Allocate Dominatee problem. Consequently, we propose an optimal centralized algorithm and an efficient probability‐based distributed algorithm to solve the Load‐balanced Allocate Dominatee problem. For a given CDS, the upper and lower bounds of the performance ratio of the distributed algorithm are analyzed in the paper. Through extensive simulations, we demonstrate that our proposed methods extend network lifetime by up to 80% compared with the most recently published CDS construction algorithm. Copyright © 2012 John Wiley & Sons, Ltd.

Summary form only given. Identification of protein complexes from protein-protein interaction network has become a key problem for understanding cellular life in post-genomic era. Many computational methods have been proposed for identifying protein complexes. Up to now, the existing computational methods are mostly applied on static PPI networks. However, proteins and their interactions are dynamic in reality. Identifying dynamic protein complexes is more meaningful and challenging. In this paper, a novel algorithm, named DPC, is proposed to identify dynamic protein complexes by integrating PPI data and gene expression profiles. Not only is the topological characters but also dynamic meaning considered in DPC. The protein complexes produced by our algorithm DPC contain two parts: static core expressed in all the molecular cycle and dynamic attachments short-lived. According to core-attachment assumption, these proteins which are always active in the molecular cycle are regarded as core proteins. The protein-complex cores are identified from these always active proteins by detecting dense sub-graphs. All possible protein complexes are extended from the protein-complex cores by adding attachments based on a topological character of “closeness”. Others which not belong to always active proteins are considered as potential attachments. On a certain time course, an attachment protein can only participate in one protein complex. Based on this idea, we first find a best protein-complex core for each potential attachment. It means that if a protein would be active at the some time, it would be added into the best protein-complex core for forming protein complexes. According to the formation and function of a protein complex, it should be active in two or more continual time courses. Based on the above analysis, we use the following rules to filter false positive complexes: 1) A protein complex should include at least two proteins; 2) The attachment proteins should be active in the same time course or in different but adjacent time courses; 3) If the attachments of a possible protein complex do not satisfy the second rule and the protein-complex core involves at least two proteins, the core will be kept as a final protein complex. So final protein complexes are extended from the protein-complex cores by adding attachments based on a topological character of “closeness” and dynamic meaning. The protein complexes produced by our algorithm DPC contain two parts: static core expressed in all the molecular cycle and dynamic attachments short-lived. The proposed algorithm DPC was applied on the data of Scaccharomves cerevisiae and the experimental results show that DPC outperforms CMC, MCL, SPICi, HC-PIN, COACH and Core-Attachment based on the validation of matching with known complexes and hF-measures.

Essential proteins are indispensable for cellular life. It is of great significance to identify essential proteins that can help us understand the minimal requirements for cellular life and is also very important for drug design. However, identification of essential proteins based on experimental approaches are always time-consuming and expensive. With the development of high-throughput technology in the post-genomic era, more and more protein-protein interaction data can be obtained, which make us study essential proteins from the network level become possible. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. Most of these topology based essential protein discovery methods were to use network centrality. In this paper, we investigate the essential proteins' topological characters from a completely new perspective. To our knowledge it is the first time that topology potential is used to identify essential proteins from protein-protein interaction network. The basic idea is that each protein in the network can be viewed as a material particle which creates a potential field around itself and the interaction of all proteins forms a topological field over the network. By defining and computing the value of each protein's topology potential, we can obtain a more precise ranking which reflects the importance of proteins from the protein-protein interaction network. The experiment results show that topology potential outperforms traditional topology measures: Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality(SC), Eigenvector Centrality(EC), Information Centrality(IC), and Sum of ECC (NC) for predicting essential proteins. In addition, these centrality measures are improved on their performance for identifying essential proteins in biological network when controlled by topology potential.

The vast amount of genes and proteins that participate in biological networks imposes the need for determination of protein complexes within the network in order to reduce the complexity, while these complexes will be the first step in deciphering the composite genetic or cellular interactions of the overall network.

Prediction of essential proteins which are crucial to an organism survival is important for disease analysis and drug design, as well as the understanding of cellular life. The majority of prediction methods infer the possibility of proteins to be essential by using the network topology. However, these methods are limited to the complementation of available protein-protein interaction (PPI) data and depend on the network accuracy. To overcome these limitation, some computational methods have been proposed while seldom of them solve this problem by taking consideration of protein domains. In this work, we firstly analyze the correlation between the essentiality of proteins and their domain features based on data of 13 species. We find that the proteins containing more protein domain types which rarely occur in other proteins tend to be essential. Accordingly we propose a new prediction method, named UDoNC, by combining the domain features of proteins with their topological properties in PPI network. In UDoNC, the essentiality of proteins is decided by the number and the frequency of their protein domain types, as well as the essentiality of their adjacent edges measured by edge clustering coefficient. The experimental results on S. cerevisiae data show that UDoNC outperforms other existing methods in terms of area under the curve (AUC).

Recent works on deep belief network (DBNs) have shown that applying large-scale unsupervised feature learning model can dramatically improve the performance of the applications in many fields. Training billions of parameters in these models such as restricted boltzmann machines (RBMs) appears to be computational challenging for modern CPUs. Graphical Processing Units (GPUs) has been employed in many large-scale deep learning models for performance enhancement due to its massively parallel computing capability. Unfortunately, the limited device memory of GPUs imposes a restriction on the size of the model trained on a single GPU. Multi-GPUs approaches, on the other hand, suffer from inefficient communication and economic cost. In this paper, we proposed a novel memory efficient algorithm on single GPU that can train large-scale RBMs without size restriction and preserve the performance gain of GPU parallel computation. Particularly, the experiments demonstrated that our approach used 75% less memory storage at the cost of only 10% performance loss in training large-scale RBMs with billions of parameters.

Attribute reduction for big data is viewed as an important preprocessing step in the areas of pattern recognition, machine learning and data mining. In this paper, a novel parallel method based on MapReduce for large-scale attribute reduction is proposed. By using this method, several representative heuristic attribute reduction algorithms in rough set theory have been parallelized. Further, each of the improved parallel algorithms can select the same attribute reduct as its sequential version, therefore, owns the same classification accuracy. An extensive experimental evaluation shows that these parallel algorithms are effective for big data.

A common and cost-effective mechanism to identify the functionalities, structures, or relationships between species is multiple-sequence alignment, in which DNA/RNA/protein sequences are arranged and aligned so that similarities between sequences are clustered together. Correctly identifying and aligning these sequence biological similarities help from unwinding the mystery of species evolution to drug design. We present our knowledge-based multiple sequence alignment (KB-MSA) technique that utilizes the existing knowledge databases such as SWISSPROT, GENBANK, or HOMSTRAD to provide a more realistic and reliable sequence alignment. We also provide a modified version of this algorithm (CB-MSA) that utilizes the sequence consistency information when sequence knowledge databases are not available. Our benchmark tests on BAliBASE, PREFAB, HOMSTRAD, and SABMARK references show accuracy improvements up to 10 percent on twilight data sets against many leading alignment tools such as ISPALIGN, PADT, CLUSTALW, MAFFT, PROBCONS, and T-COFFEE.

The security and privacy preservation issues are prerequisites for vehicular ad hoc networks. Recently, secure and privacy enhancing communication schemes (SPECS) was proposed and focused on intervehicle communications. SPECS provided a software-based solution to satisfy the privacy requirement and gave lower message overhead and higher successful rate than previous solutions in the message verification phase. SPECS also presented the first group communication protocol to allow vehicles to authenticate and securely communicate with others in a group of known vehicles. Unfortunately, we find out that SPECS is vulnerable to impersonation attack. SPECS has a flow such that a malicious vehicle can force arbitrary vehicles to broadcast fake messages to other vehicles or even a malicious vehicle in the group can counterfeit another group member to send fake messages securely among themselves. In this paper, we provide a secure scheme that can achieve the security and privacy requirements, and overcome the weaknesses of SPECS. Moreover, we show the efficiency merits of our scheme through performance evaluations in terms of verification delay and transmission overhead.

Essential proteins are vital for an organism's viability under a variety of conditions. Computational prediction of essential proteins based on the global protein-protein interaction (PPI) network is severely restricted because of the insufficiency of the PPI data, but fortunately the gene expression profiles help to make up the deficiency. In this work, Pearson correlation coefficient (PCC) is used to bridge the gap between PPI and gene expression data. Based on PCC and Edge Clustering Coefficient (ECC), a new centrality measure, i.e., the weighted degree centrality (WDC), is developed to achieve the reliable prediction of essential proteins. WDC is employed to identify essential proteins in the yeast PPI network in order to estimate its performance. For comparison, other prediction technologies are also performed to identify essential proteins. Some evaluation methods are used to analyze the results from various prediction approaches. The analyses prove that WDC outperforms other state-of-the-art ones. At the same time, the analyses also mean that it is an effective way to predict essential proteins by means of integrating different data sources.

Advanced mobile devices such as smartphones have become a part of daily lives for digital native students today. We believe that the integration of mobile computing into computer science (CS) curricula with smart mobile devices based learning tools will connect the education to leading-edge information technologies, motivate and engage students in CS learning, innovate CS education, fill in the gap between academia and industry, and help faculty build strong expertise on modern mobile computing. Meanwhile, the hands-on experience with mobile computing will promote student learning with a better understanding of the fundamental CS concepts. In this paper, we propose to design and develop a labware based on Android-enabled smartphones to introduce mobile computing knowledge and practice into the CS curriculum, covering a wide range of CS subjects.

Cloud computing is an increasingly important solution for providing services deployed in dynamically scalable cloud networks. Services in the cloud computing networks may be virtualized with specific servers which host abstracted details. Some of the servers are active and available, while others are busy or heavy loaded, and the remaining are offline for various reasons. Users would expect the right and available servers to complete their application requirements. Therefore, in order to provide an effective control scheme with parameter guidance for cloud resource services, failure detection is essential to meet users' service expectations. It can resolve possible performance bottlenecks in providing the virtual service for the cloud computing networks. Most existing Failure Detector (FD) schemes do not automatically adjust their detection service parameters for the dynamic network conditions, thus they couldn't be used for actual application. This paper explores FD properties with relation to the actual and automatic fault-tolerant cloud computing networks, and find a general non-manual analysis method to self-tune the corresponding parameters to satisfy user requirements. Based on this general automatic method, we propose specific and dynamic Self-tuning Failure Detector, called SFD, as a major breakthrough in the existing schemes. We carry out actual and extensive experiments to compare the quality of service performance between the SFD and several other existing FDs. Our experimental results demonstrate that our scheme can automatically adjust SFD control parameters to obtain corresponding services and satisfy user requirements, while maintaining good performance. Such an SFD can be extensively applied to industrial and commercial usage, and it can also significantly benefit the cloud computing networks.

Advanced mobile devices such as smart phones and tablets have become a part of daily lives for digital native students today. The integration of mobile computing with these devices into multiple courses in computer science (CS) curriculum will connect the education to leading-edge information technologies, motivate and engage students in CS learning, fill in the gap with IT industry needs, and help faculties build strong expertise on modern mobile computing. Meanwhile, the hands-on experience with mobile computing will supply the students with a better understanding of the classical CS concepts. In this paper, we propose to develop a cross-curriculum labware based on the Android platform to introduce the knowledge and practice of a wide range of CS subjects with smart phones and tablets.

BACKGROUND: Identification of protein complexes and functional modules from protein-protein interaction (PPI) networks is crucial to understanding the principles of cellular organization and predicting protein functions. In the past few years, many computational methods have been proposed. However, most of them considered the PPI networks as static graphs and overlooked the dynamics inherent within these networks. Moreover, few of them can distinguish between protein complexes and functional modules. RESULTS: In this paper, a new framework is proposed to distinguish between protein complexes and functional modules by integrating gene expression data into protein-protein interaction (PPI) data. A series of time-sequenced subnetworks (TSNs) is constructed according to the time that the interactions were activated. The algorithm TSN-PCD was then developed to identify protein complexes from these TSNs. As protein complexes are significantly related to functional modules, a new algorithm DFM-CIN is proposed to discover functional modules based on the identified complexes. The experimental results show that the combination of temporal gene expression data with PPI data contributes to identifying protein complexes more precisely. A quantitative comparison based on f-measure reveals that our algorithm TSN-PCD outperforms the other previous protein complex discovery algorithms. Furthermore, we evaluate the identified functional modules by using "Biological Process" annotated in GO (Gene Ontology). The validation shows that the identified functional modules are statistically significant in terms of "Biological Process". More importantly, the relationship between protein complexes and functional modules are studied. CONCLUSIONS: The proposed framework based on the integration of PPI data and gene expression data makes it possible to identify protein complexes and functional modules more effectively. Moveover, the proposed new framework and algorithms can distinguish between protein complexes and functional modules. Our findings suggest that functional modules are closely related to protein complexes and a functional module may consist of one or multiple protein complexes. The program is available at http://netlab.csu.edu.cn/bioinfomatics/limin/DFM-CIN/index.html.

Discovering network motifs could provide a significant insight into systems biology. Interestingly, many biological networks have been found to have a high degree of symmetry (automorphism), which is inherent in biological network topologies. The symmetry due to the large number of basic symmetric subgraphs (BSSs) causes a certain redundant calculation in discovering network motifs. Therefore, we compress all basic symmetric subgraphs before extracting compressed subgraphs and propose an efficient decompression algorithm to decompress all compressed subgraphs without loss of any information. In contrast to previous approaches, the novel Symmetry Compression method for Motif Detection, named as SCMD, eliminates most redundant calculations caused by widespread symmetry of biological networks. We use SCMD to improve three notable exact algorithms and two efficient sampling algorithms. Results of all exact algorithms with SCMD are the same as those of the original algorithms, since SCMD is a lossless method. The sampling results show that the use of SCMD almost does not affect the quality of sampling results. For highly symmetric networks, we find that SCMD used in both exact and sampling algorithms can help get a remarkable speedup. Furthermore, SCMD enables us to find larger motifs in biological networks with notable symmetry than previously possible.

Identification of essential proteins is key to understanding the minimal requirements for cellular life and important for drug design. The rapid increase of available protein-protein interaction (PPI) data has made it possible to detect protein essentiality on network level. A series of centrality measures have been proposed to discover essential proteins based on network topology. However, most of them tended to focus only on the location of single protein, but ignored the relevance between interactions and protein essentiality. In this paper, a new centrality measure for identifying essential proteins based on edge clustering coefficient, named as NC, is proposed. Different from previous centrality measures, NC considers both the centrality of a node and the relationship between it and its neighbors. For each interaction in the network, we calculate its edge clustering coefficient. A node’s essentiality is determined by the sum of the edge clustering coefficients of interactions connecting it and its neighbors. The new centrality measure NC takes into account the modular nature of protein essentiality. NC is applied to three different types of yeast protein-protein interaction networks, which are obtained from the DIP database, the MIPS database and the BioGRID database, respectively. The experimental results on the three different networks show that the number of essential proteins discovered by NC universally exceeds that discovered by the six other centrality measures: DC, BC, CC, SC, EC, and IC. Moreover, the essential proteins discovered by NC show significant cluster effect.

Various gene-expression signatures for breast cancer are available for prediction of clinical outcome, but due to small overlap between different signatures, it is challenging to integrate existing disjoint signatures to provide a unified insight on the association between gene expression and clinical outcome. In this paper, we proposed a method to identify reliable breast cancer gene signature from a context-constrained protein interaction network(PIN). The context-constrained PIN for breast cancer is built by integrating complete PIN and various gene signatures reported in literature. Then, we used graph centrality to quantify the importance of genes to breast cancer. Finally, we got reliable gene signatures that are consisted by the genes with high graph centrality. The genes which are well- known breast cancer genes, such as TP53 and BRCA1 are ranked extremely high in our results. Compared with previous result by functional enrichment analysis, graph centrality, especially the eigenvector centrality and subgraph centrality based gene signatures are more tightly related to breast cancer. We validated these signatures on genome-wide microarray dataset and found higher relationship between the expression of these signature genes and pathologic parameters. In summary, graph centrality provides a novel way to connect different cancer signatures and to understand the mechanism of relationship between gene expression and clinical outcome of breast cancer. Moreover, this method is applied not only to breast cancer, but also to other gene expression related diseases.

Essential proteins are indispensable to support cellular life and constitute a minimal set required for a living cell. Fast progress in high-throughput technologies and large amount of data enable to discover essential proteins in system level by analyzing protein-protein interaction networks. A number of centrality algorithms are suggested to detect essential proteins, but they focus only on network structures. In this paper, we develop a new centrality algorithm, named MCGO which uses network motifs for centrality measure in the graph pruned by EDGEGO. EDGEGO algorithm utilizes Gene Ontology(GO) to trim a number of uninformative edges from the network. We compare the performance of our algorithm with DC (degree centrality) and SoECC (sum of edge clustering coefficient) against various evaluation measures. Experimental results applied to an yeast protein-protein interaction network downloaded from DIP database show that MCGO performs significantly better than DC and SoECC. We also show that DC and SoECC improve greatly when EDGEGO is applied to them.

Clustering of protein-protein interaction networks is one of the most prevalent methods for identifying protein complexes and functional modules, which is crucial to understanding the principles of cellular organization and prediction of protein functions. In the past few years, many computational methods have been proposed. However, it is always a challenging task to evaluate how well the clusters are identified. Even for the most popular measurements, F-measure and Pvalue, bias exists for evaluating the identified clusters. In this paper, we propose a new measurement, named hF-measure, to evaluate clusters more finely and distinctly. First, we defined the hierarchical consistency and the hierarchical similarity. Then, we propose a new hierarchical measurement of hF-measure by taking into account the hierarchical organization of functional annotations and the functional similarities among proteins. The new measurement hF-measure can discriminate between different types of errors which cannot be distinguished by F-measure. The experimental results based on Gene Ontology (GO) and yeast functional modules show that hF-measure evaluates clusters more accurately when compared to F-measure.

In recent years, more and more attentions are focused on modelling and analyzing dynamic network. Some researchers attempted to extract dynamic network by combining the dynamic information from gene expression data or subcellular localization data with protein network. However, the dynamics of proteins' presence does not guarantee the dynamics of interactions, since the presence of a protein does not indicate the protein's activity. The activity of a protein is closely connected with its function. Thus only the dynamics of proteins activity ensure the dynamics of interaction. The gene expression of a cellular process or cycle carries more information than only the dynamics of proteins' presence. We assume that a protein is active when its expression values are near its maximum expression value, since the expression quantity will decrease after it has performed its function that leads a feedback for controlling the expression quantity. In this paper, we proposed a method to identify active time points for each protein in a cellular process or cycle by using a 3-sigma principle to compute an active threshold for each gene according to the characteristics of its expression curve. Combined the activity information and protein interaction network, we can construct an active protein interaction network (APPI). To demonstrate the efficiency of APPI network model, we applied it on complex detection. Compared with single threshold time series networks, APPI network achieves a better performance on protein complex prediction.

In OFDM-based optical networks, multiple subcarriers can be allocated to accommodate various size of traffic demands. By using the multi-carrier modulation technique, subcarriers for the same node-pair can be overlapping in the spectrum domain. Compared to the traditional wavelength routed networks (WRNs), the OFDM-based Spectrum-sliced Elastic Optical Path (SLICE) network has higher spectrum efficiency due to its finer granularity and frequency-resource saving. In this work, for the first time, we comprehensively study the routing and spectrum allocation (RSA) problem in the SLICE network. After proving the NP-hardness of the static RSA problem, we formulate the RSA problem using the Integer Linear Programming (ILP) formulations to optimally minimize the maximum number of sub-carriers required on any fiber of a SLICE network. We then analyze the lower/upper bounds for the sub-carrier number in a network with general or specific topology. We also propose two efficient algorithms, namely, balanced load spectrum allocation (BLSA) algorithm and shortest path with maximum spectrum reuse (SPSR) algorithm to minimize the required sub-carrier number in a SLICE network. The results show that the proposed algorithms can match the analysis and approximate the optimal solutions using the ILP model.

BACKGROUND: Cellular systems are highly dynamic and responsive to cues from the environment. Cellular function and response patterns to external stimuli are regulated by biological networks. A protein-protein interaction (PPI) network with static connectivity is dynamic in the sense that the nodes implement so-called functional activities that evolve in time. The shift from static to dynamic network analysis is essential for further understanding of molecular systems. RESULTS: In this paper, Time Course Protein Interaction Networks (TC-PINs) are reconstructed by incorporating time series gene expression into PPI networks. Then, a clustering algorithm is used to create functional modules from three kinds of networks: the TC-PINs, a static PPI network and a pseudorandom network. For the functional modules from the TC-PINs, repetitive modules and modules contained within bigger modules are removed. Finally, matching and GO enrichment analyses are performed to compare the functional modules detected from those networks. CONCLUSIONS: The comparative analyses show that the functional modules from the TC-PINs have much more significant biological meaning than those from static PPI networks. Moreover, it implies that many studies on static PPI networks can be done on the TC-PINs and accordingly, the experimental results are much more satisfactory. The 36 PPI networks corresponding to 36 time points, identified as part of this study, and other materials are available at http://bioinfo.csu.edu.cn/txw/TC-PINs.

As advances in the technologies of predicting protein interactions, huge data sets portrayed as networks have been available. Identification of functional modules from such networks is crucial for understanding principles of cellular organization and functions. However, protein interaction data produced by high-throughput experiments are generally associated with high false positives, which makes it difficult to identify functional modules accurately. In this paper, we propose a fast hierarchical clustering algorithm HC-PIN based on the local metric of edge clustering value which can be used both in the unweighted network and in the weighted network. The proposed algorithm HC-PIN is applied to the yeast protein interaction network, and the identified modules are validated by all the three types of Gene Ontology (GO) Terms: Biological Process, Molecular Function, and Cellular Component. The experimental results show that HC-PIN is not only robust to false positives, but also can discover the functional modules with low density. The identified modules are statistically significant in terms of three types of GO annotations. Moreover, HC-PIN can uncover the hierarchical organization of functional modules with the variation of its parameter's value, which is approximatively corresponding to the hierarchical structure of GO annotations. Compared to other previous competing algorithms, our algorithm HC-PIN is faster and more accurate.

Abstract Wireless monitoring and control technique is vital to improve the performance of wireless networks, e.g., helping improve the efficiency of scheduling. In this paper, we focus on two centralized algorithms based on IEEE 802.16 standard to adopt effective monitor and control mechanism to do the scheduling and channel assignment in WiMAX mesh networks. The base station (BS) monitors the subscriber stations (SSs) and gathers their requests, and then controls the SSs under the direction of the centralized algorithm. The IEEE 802.16 standard proposes two kinds of scheduling algorithms in WiMAX mesh networks, centralized and distributed algorithms. The centralized solution is more convenient to monitor and control a WiMAX mesh network since the central node is the unique monitor and controller. This paper mainly introduces two schemes working in a centralized mode. They are working in a tree search active link selection (TSALS) fashion and a link search active link selection (LSALS) fashion. In these schemes, each node is equipped with a third generation (3G) mesh router, which owns two relay transceivers and can be tuned among multiple channels. The major goal of our solution is to reduce the length of scheduling under an interference‐aware restriction. The feasibility and efficiency of our proposal are testified through extensive simulations. The result justifies that our algorithms provide much higher performance than the previous ones. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract Next generation communications (NGC) represents advanced communication environments featuring objects that are focused on users. It is crucial to protect information and digital forensics for NGC. In this paper, we aim to address network security for NGC, including IEEE standard 802.16 that are associated with NGC utilization. The IEEE standard 802.16 is a wireless air interface technology in Wireless Metropolitan Access Network (WMAN). Now in the new era of wireless networking, it is the solution for ‘last mile’ problem. Point‐to‐MultiPoint (PMP) network architecture network mode and mesh network mode are two optional networking modes proposed in this standard. However, the security problems in its original protocol may be becoming the most serious obstacle in its marketable producing process. In this paper, explanation and analysis of the security mechanism are based on PMP and mesh networking modes, respectively. This paper focuses on authentication & authorization, which is the most important part but not explicitly defined in original security design of IEEE standard 802.16. Two improved schemes focused on authentication & authorization based on the two modes are proposed to strengthen 802.16 WMAN security, respectively. The relevant analysis and simulation results prove that our schemes are effective. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract The rapid development of wireless technology plays extremely important roles in monitoring, control, and collaboration related applications. This paper aims at state‐of‐the‐art self‐stabilizing flocking control of wireless mobile robots with memory corruption. Flocking of a group of wireless mobile robots has gained a lot of attention due to its wide applications in recent years. While there are few works addressed on fault tolerance issue of flocking, especially for transient failure of robots. However, in the practical applications of robots, for the weak robots, some of their parts, like sensor, moving actuator, or memory etc., are prone to be crashed due to the complex environment. So, it is interesting to explore such kind of component crash or corruption (partly crash), especially for transient failure. The transient failure means the failure is temporary and after a while the robots will not be influenced by outside environment. Specially, in this paper, we mainly focus on how one kind of transient failure—memory corruption affects robot flocking, especially for the existed fault tolerant flocking. Copyright © 2009 John Wiley & Sons, Ltd.

The six papers in this special section cover a broad range of bioinformatics topics, ranging from comparative genomics and phylogenetics to population genetics, and from RNA structure prediction to analysis of protein-protein interaction networks.

Radio frequency identification has been developed and used in many applications in the real world. Due to the shared wireless channel between tags and the reader during communication, the tag collision arbitration is a significant issue for reducing the communication overhead. This paper presents a novel anti-collision algorithm named New Enhanced Anti-Collision Algorithm (NEAA) using counters and stack to reduce the probability of collision efficiently and to make it possible to identify multiple passive tags in a timeslot. The upper bound of total timeslots for identifying N passive tags is first derived in this paper; suppose the length of a tag ID is n, the upper bound of total timeslots for identifying N (N= 2n) passive tags is derived to be 2n-1- n + 4, when n > 2. This bound is quite tight. Compared to the existing methods proposed by other researchers, the performance evaluation shows that the proposed scheme in this paper consumes fewer timeslots and has better performance for identifying tags.

Wireless sensor networks can be used for event warning applications. Till date, in most of the proposed schemes, the raw or aggregated sensed data are periodically sent to a data consuming center. However, with those schemes, the occurrence of an emergency event such as a fire is hardly reported timely, which is a strict requirement for event warning applications. In wireless sensor networks, it is also highly desired to conserve energy so that network lifetime can be maximized. Furthermore, to ensure the quality of surveillance, some applications require that if an event occurs, it needs to be detected by at least k sensors, where k is a user-defined parameter. In this work, we examine the Timely Energy-efficient k-Watching Event Monitoring (TEKWEM) problem and propose a scheme, which involves an event detection model and a warning delivery model, for monitoring composite events and delivering warnings to users. Theoretical analysis and simulation results are shown to validate the proposed scheme.

This paper proposes a novel and efficient distributed flow control scheme for multirate multicast (MR-M), based on the well-known Proportional Integral and Derivative (PID) controllers. The PID controller at each router computes its expected incoming rate and feed backs this rate to its upstream router, such that the local buffer occupancy can be stabilized at an appropriate value. We give the theoretical analysis of the proposed PID controller in terms of system stability. The proposed MR-M controller achieves the fairness in two aspects: 1) The intrasession fairness, i.e., the receivers from the same source within the same multicast session can receive data at different rates, if they subscribe networks with different capacities; 2) The intersession fairness, i.e., the link bandwidth is fairly shared among multiple multicast sessions from different sources. Extensive simulations have been conducted and the results have demonstrated a superior performance of the proposed scheme in terms of system stability, high link utilization, and high throughput.

With the ever-increasing wireless/wired data applications recently, considerable efforts have focused on the design of distributed explicit rate flow control schemes for multi-input-multi-output service. This paper describes two novel wireless/wired multipoint-to-multipoint multicast flow control schemes, which are based on the distributed self-tuning proportional integrative plus derivative (SPID) controller and distributed self-tuning proportional plus integrative (SPI) controller, respectively. The control parameters can be designed to ensure the stability of the control loop in terms of source rate. The distributed explicit rate SPID and SPI controllers are located at the wireless/wired multipoint-to-multipoint multicast source to regulate the transmission rate. We further analyze the theoretical aspects of the proposed algorithm, and show how the control mechanism can be used to design a controller to support wireless/wired multipoint-to-multipoint multicast transmissions. Simulation results demonstrate the efficiency of the proposed scheme in terms of system stability, fast response, low packet loss, and high scalability, and the results also show SPID scheme has better performance than SPI scheme, however, SPID scheme requires more computing time and CPU resource.

Recent work using supervised learning for protein structure prediction has achieved state-of-the-art classification performance. However, such methods are based only on labeled data, while in practice the labeled data is so few and expensive to obtain and unlabeled data is far more plentiful. An effective way to enhance the performance of the learned hypothesis by using the labeled and unlabeled data together is known as semi-supervised learning. Although there are lots of semi-supervised learning methods, those approaches could not always achieve the acceptable results for bioinformatics application, especially when there is only very few labeled instances. Therefore, in this paper, we present a novel, more effective method tri-cluster-tri-scheme-training (TCTS) which firstly uses tri-cluster to label some high confidence unlabeled instances and then refines the classifiers by utilizing both of the label data and unlabeled data in the Tri-Scheme-training by different schemes. The encouraging experimental results indicate that TCTS algorithm opens a new way to solve the complex classification problem when very few labeled datasets are available.

Welcome to the 10th IEEE International Workshop on Parallel and Distributed Scientific and Engineering Computing (PDSEC-09), held on 29 May 2009 in Rome, Italy, in conjunction with the 23rd IEEE Int. Parallel and Distributed Processing Symposium (IPDPS 2009).

With the ever-increasing wireless multicast data applications recently, considerable efforts have focused on the large scale heterogeneous wireless multicast, especially those with large propagation delays, which means the feedbacks arriving at the source node are somewhat outdated and harmful to the control actions. To attack the above problem, this paper describes a novel, autonomous, and predictive wireless multicast flow control scheme, the so-called proportional, integrative plus neural network (PINN) predictive technique, which includes two components: the PI flow controller located at the wireless multicast source has explicit rate algorithm to regulate the transmission rate; and the neural network part located at the middle branch node predicts the available buffer occupancy for those longer delay receivers. The ultimate sending rate of the multicast source is the expected receiving rates computed by PI controller based on the consolidated feedback information, and it can be accommodated by its participating branches. This network-assisted property is different from the existing control schemes in that neural network controller can predict the buffer occupancy caused by those long delay receivers, which probably cause irresponsiveness of a wireless multicast flow. This active scheme makes the control more responsive to the network status, therefore, the rate adaptation can be in a timely manner for the sender to react to network congestion quick. We analyze the theoretical aspects of the proposed algorithm, show how the control mechanism can be used to design a controller to support wireless multi-rate multicast transmission based on feedback of explicit rates.

Failure detection (FD) is an important issue for supporting dependability in distributed healthcare systems to guarantee continuous, safe, secure, and dependable operation, and often is an important performance bottleneck in the event of node failure. FD can be used to manage the health status of communication for delivering telemedicine services, and then to help distributed healthcare system reduce fatal accident rate and increase the reliability and safety of systems. Ensuring acceptable quality of service (QoS) is made difficult by the relative unpredictability of the network environment. In this paper, first, we compare QoS metrics of several adaptive FDs, discuss their properties and their relation, and then propose one optimization over the existing methods, called tuning adaptive margin failure detector (TAM FD), which significantly improves QoS, especially in the aggressive range and when the network is unstable. Second, we address the problem of most adaptive schemes, namely their need for a large window of samples. So we also analyze the impact of memory size on the performance of FDs, and then prove that the presented scheme is designed to use a fixed and very limited amount of memory for the distributed system. Our experimental results over several kinds of networks (Cluster, WiFi, LAN, Intercontinental WAN) show that the properties of the existing adaptive failure detectors, and demonstrate that the optimization is reasonable and acceptable. Furthermore, the extensive experimental results show what is the effect of memory size on the overall QoS of each adaptive failure detector. For our TAM FD, the effect of window size on their QoS is very small and can be negligible.

The number of virtual channels required for deadlock-free routing is important for cost-effective and high-performance system design. The planar adaptive routing scheme is an effective deadlock avoidance technique using only three virtual channels for each physical channel in 3D or higher dimensional mesh networks with a very simple deadlock avoidance scheme. However, there exist one idle virtual channel for all physical channels along the first dimension and two idle virtual channels for channels along the last dimension in a mesh network based on the planar adaptive routing algorithm. A new deadlock avoidance technique is proposed for 3D meshes using only two virtual channels by making full use of the idle channels. The deadlock-free adaptive routing scheme is then modified to a deadlock-free adaptive fault-tolerant routing scheme based on a planar network (PN) fault model. The proposed deadlock-free adaptive routing scheme is also extended to n-dimensional meshes still using two virtual channels. Sufficient simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

The five papers in this special section were presented at the Fourth International Symposium on Bioinformatics Research and Application (ISBRA), held at Georgia State University in Atlanta, GA, on 6-9 May 2008.

Fixed-power wireless sensor networks are prevalent and cost-effective. However, they face mote failures, RF interference from environmental noise and energy constraints. Routing protocols for such networks must overcome these problems to achieve reliability, energy efficiency and scalability in message delivery. Achievement of these requirements, however, poses conflicting demands. In this paper, we propose an efficient and reliable routing protocol (EAR) that achieves reliable and scalable performance with minimal compromise of energy efficiency. The routing design of EAR is based on four parameters - expected path length and a weighted combination of distance traversed, energy levels and link transmission success history, to dynamically determine and maintain the best routes. Simulation experiments of EAR with four existing protocols demonstrate that a design based on a combination of routing parameters exhibits collectively better performance than protocols based on just hop-count and energy or those using flooding.

Protein sequence motifs information is crucial to the analysis of biologically significant regions. The conserved regions have the potential to determine the role of the proteins. Many algorithms or techniques to discover motifs require a predefined fixed window size in advance. Due to the fixed size, these approaches often deliver a number of similar motifs simply shifted by some bases or including mismatches. To confront the mismatched motifs problem, we use the super-rule concept to construct a Super-Rule-Tree (SRT) by a modified HHK clustering which requires no parameter setup to identify the similarities and dissimilarities between the motifs. By analyzing the motifs results generated by our approach, they are not only significant in sequence area but secondary structure similarity. We believe new proposed HHK clustering algorithm and SRT can play an important role in similar researches which requires predefined fixed window size.

A new deadlock-free fully adaptive routing algorithm is proposed for 2-dimensional tori with only two virtual channels. The deadlock avoidance technique is presented based on a new virtual network partitioning scheme. Unlike the previous virtual network partitioning schemes, the new method allows all virtual networks to share some common virtual channels. Two virtual channels should be the lower bound for fully adaptive deadlock-free routing in tori because the dimension order routing for 2-dimensional tori also needs two virtual channels. The proposed virtual network partitioning scheme can avoid all potential deadlocks and provides fully adaptive routing. Enough theoretical analyses on the proposed virtual network partitioning scheme are presented. Sufficient simulation results are presented to demonstrate the effectiveness of the proposed algorithm by comparing with the dimension-order routing, two partially adaptive routing schemes, Duato's protocol and the load-balanced routing algorithm GOAL.

A new, deadlock-free, fully adaptive routing algorithm is proposed for worm hole-switched 3-dimensional tori with only two virtual channels. The deadlock avoidance technique is presented based on a new virtual network partitioning scheme. Unlike the previous virtual network partitioning schemes, the new method allows all virtual networks to share some common virtual channels. A new virtual channel assignment scheme is proposed for the 3-dimensional mesh subnetwork by using a channel overlap scheme. A combination of the virtual network partitioning scheme and the channel overlap scheme provides a deadlock-free fully adaptive routing for 3-dimensional tori. Sufficient theoretical analysis on the proposed virtual network partitioning scheme is presented. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm by comparing with several important previous methods.

Public computing is a type of grid computing architectures composed of autonomous volunteer workstations coordinated by a central server complex over the Internet. Exploiting these distributed resources comes at the price of unpredictable availability, fluctuating performance, and heterogeneous participant nodes. Central to addressing these problems is an efficient and accurate scheduling mechanism for the public computing architecture. In this talk, I will overview grid computing in general and a public computing platform called BOINC in particular. Several major issues and challenges in grid computing research will be identified. I will also report our new research results in this exciting area through introducing a scheduling system based on the ant colony algorithm. In addition, the bottleneck problem in the client-server architecture used in BOINC will be identified and a new peer-to-peer architecture for solving the problem will be presented. Several applications have been implemented on BOINC and our new architecture using our scheduling system. By testing these applications with totally different characteristics, we show that our scheduling system consistently provides a high performance, adaptive solution to all of them, and the system using our new peer-to-peer architecture indeed performs better than BOINC. Our work also manifests that public computing can solve the problems of large computing power requirement and huge memory demand in many scientific applications and potentially replace high-cost supercomputing systems for certain application domains.

A call admission control scheme named thinning scheme, which smoothly throttles the admission rates of calls according to their priorities and aims to provide multiple prioritized traffic with a desired quality of service, is investigated under more general conditions. Using the theory of multidimensional Markov birth-death process, analytical formulas for call blocking probabilities are derived.

The special section includes a selection of papers presented at the Third International Symposium on Bioinformatics Research and Application (ISBRA 2007). which took place at Georgia State University, Atlanta, 7-10 May 2007.

This paper proposes a blind watermarking algorithm based on the significant difference of wavelet coefficient quantization for copyright protection. Every seven nonoverlap wavelet coefficients of the host image are grouped into a block. The largest two coefficients in a block are called significant coefficients in this paper and their difference is called significant difference. We quantized the local maximum wavelet coefficient in a block by comparing the significant difference value in a block with the average significant difference value in all blocks. The maximum wavelet coefficients are so quantized that their significant difference between watermark bit 0 and watermark bit 1 exhibits a large energy difference which can be used for watermark extraction. During the extraction, an adaptive threshold value is designed to extract the watermark from the watermarked image under different attacks. We compare the adaptive threshold value to the significant difference which was quantized in a block to determine the watermark bit. The experimental results show that the proposed method is quite effective against JPEG compression, low-pass filtering, and Gaussian noise; the PSNR value of a watermarked image is greater than 40 dB.

Yi Pan is the chair and a professor in the Department of Computer Science and a professor in the Department of Computer Information Systems at Georgia State University. Dr. Pan received his B.Eng. and M.Eng. degrees in computer engineering from Tsinghua University, China, in 1982 and 1984, respectively, and his Ph.D. degree in computer science from the University of Pittsburgh, USA, in 1991.

Protein sequence motifs are gathering more and more attention in the sequence analysis area. These recurring regions have the potential to determine protein 's conformation, function and activities. In our previous work, we tried to obtain protein sequence motifs which are universally conserved across protein family boundaries. Therefore, unlike most popular motif discovering algorithms, our input dataset is extremely large. In order to deal with large input datasets, we provided two granular computing models (FIK and FGK model) to efficiently generate protein motifs information and Super GSVM-FE model to do the feature elimination for improving the quality of motif information. In this article, we tried to further improve our SVM feature elimination model to achieve three goals: Reduce time execution by half, further improve motif information quality and add the ability of adjusting the number of filtered segments. Compared with the latest results, our new approach shows great improvements.

Aligning multiple homologous protein sequences (MSA) helps biologists identify the relationship between species and possibly predict the structure and functionality of the protein. However, optimally aligning multiple sequences has been proven to be intractable by Wang and Jiang in [1]. For the last two decades, researchers have often taken different heuristic approaches to solve this problem without a consistent and reliable scoring method. In this paper, we have developed a scoring metric (hierarchical expected matching probability [HEP]), that measures the probability of residue mutations and the biological correctness of MSA results. Both theoretical and manual selected test sequences have shown that our quantitative metric is more reliable, consistent, and biologically meaningful than many commonly used scoring metrics.

Understanding protein structures is vital to determining the function of a protein and its interaction with DNA, RNA and enzyme. The information about its conformation can provide essential information for drug design and protein engineering. While there are over a million known protein sequences, only a limited number of protein structures are experimentally determined. Hence, prediction of protein structures from protein sequences using computer programs is an important step to unveil proteins' three dimensional conformation and functions. As a result, prediction of protein structures has profound theoretical and practical influence over biological study. The explanation of how a decision is made during prediction is also important for improving protein structure prediction and guiding the "wet experiments". In this talk, we will show how to use machine learning methods to improve the accuracy of protein structure prediction and to interpret prediction results. We will report our research on using neural networks, support vector machines combined with decision tree and association rule for protein structure prediction, rule extraction and prediction interpretation. Evaluation and comparisons of various prediction and rule extraction systems will be presented and future research direction in this area will also be identified.

Local protein structure prediction is a central task in bioinformatics research. Local protein structure prediction can be transformed into the multiclass problem for huge datasets. In previous study, multiclass clustering support vector machines (CSVMs) was proposed for local protein structure prediction. The greedy algorithm is utilized to select the next closest class if CSVM modeled for the assigned class predicts the sequence segment as negative. However, the greedy algorithm may not be optimal. If all CSVM predict the sequence segment as negative, this sequence segment cannot be classified. In order to further improve performance of the multiclass problem, we propose fuzzy clustering support vector machines (FCSVMs) in this study. The FCSVMs model calculates the class membership value of the given sequence segment for each class and assigns the representative structure of the finally selected class to the sequence segment. Values of the fuzzy membership function are based on testing accuracy of decision function outputs from FCSVMs. Under this mechanism, values of different fuzzy membership functions can be compared. FCSVMs are built specifically for each class partitioned intelligently by the clustering algorithm. This feature makes learning tasks for each FCSVM more specific and simpler. Furthermore, FCSVM modeled for each class can be easily parallelized to handle the complex multiclass problems for huge datasets. Using fuzzy membership functions, all sequence segments can be classified. Compared with the conventional clustering algorithm and CSVMs, testing accuracy for local structure prediction has been improved noticeably when the FCSVMs model is applied.

Protein sequence motifs are gathering more and more attention in the sequence analysis area. These recurring regions have the potential to determine protein's conformation, function and activities. In our previous work, we tried to obtain protein sequence motifs which are universally conserved across protein family boundaries. Therefore, unlike most popular motif discovering algorithms, our input dataset is extremely large. In order to deal with large input datasets, we provided two granular computing models (FIK and FGK model) to efficiently generate protein motifs information. In this article, we develop a new method which combines the concept of granular computing and the power of ranking SVM to further extract protein sequence motif information. There are two reasons to eliminate redundant data: First, the information we try to generate is about sequence motifs, but the original input data are derived from whole protein sequences by a sliding window technique; second, during fuzzy c-means clustering, it has the ability to assign one segment to more than one information granule. However, not all data segments have a direct relation to the granule they assigned. The quality of motif information increases dramatically in all three evaluation measures by applying this new feature elimination model. Compared with traditional methods which shrink cluster size to obtain a more compact one, our approach shows improved results.

With the efforts to understand protein structure, many computational approaches have been made recently. Among them, the support vector machine (SVM) methods have been recently applied and showed successful performance compared with other machine learning schemes. However, despite the high performance, the SVM approaches suffer from the problem of understandability since it is a black-box model. To overcome this limitation, this study attempted to combine the SVM with the association rule based classifier which can present the meaningful explanation about the prediction. To perform this task, a new association rule based classifier (PCPAR) was devised based on the existing classifier, CPAR, to handle the sequential data. PCPAR creates the patterns by merging the generated rules and then classifies the sequential data based on the pattern match. The experimental result presents the following: with sequential data, the PCPAR scheme shows better performance with respect to the accuracy and the number of generated patterns than CPAR method whether applied alone or combined with SVM. The combined scheme of SVMPCPAR generates more compact patterns than the combined scheme of SVM with decision tree, SVM DT, with similar performance. These patterns are easily understandable and biologically meaningful

A new deadlock-free adaptive routing algorithm is proposed for n-dimensional meshes with only two virtual channels, where a virtual channel can be shared by two consecutive planes without any cyclic channel dependency. A message is routed along a series of planes. The proposed planar adaptive routing algorithm is enhanced to a fully adaptive routing version for 3-dimensional meshes using the idle virtual channels along the last dimension. Another deadlock avoidance technique is proposed for 3- dimensional meshes using a new virtual network partitioning scheme with only two virtual channels. Two virtual networks can share some common virtual channel based on the virtual network partitioning scheme. The deadlock-free adaptive routing scheme is then modified to a deadlock-free adaptive fault-tolerant routing scheme based on a planarly constructed MCC fault model. Sufficient simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

In this paper, a program is first divided into several data computing blocks (DCBs) by the branch instruction; each DCB can then be recognized as an image. We then use the one dimension discrete cosine transform (1-D DCT) to compute each DCB to generate several signatures including 5-bits relay DCT signature (R-DCT-S) and 32-bits final DCT signature (F-DCT-S). These generated signatures are embedded into the instruction memory and then used to do the run time error checking. The watchdog should not reduce the processor performance, not increase the fault detection latency and not increase the memory overhead to store the signatures; in this paper, the processor degradation can be improved by doing the whole block error checking after the branch instruction, the fault detection latency is improved by doing the intermediate error checking at the R-type instruction, and the memory overhead is reduced by storing the R-DCT-S to the R-type instruction. The experimental results show that the proposed watchdog has very high error detection coverage and shortest error detection latency to detect either single fault or multi-faults, no matter what the fault is transient or intermittent.

Grid computing is a recently developed technology. Although the developmental tools and techniques for the grid have been extensively studied, grid reliability analysis is not easy because of its complexity. This paper is the first one that presents a hierarchical model for the grid service reliability analysis and evaluation. The hierarchical modeling is mapped to the physical and logical architecture of the grid service system and makes the evaluation and calculation tractable by identifying the independence among layers. Various types of failures are interleaved in the grid computing environment, such as blocking failures, time-out failures, matchmaking failures, network failures, program failures, and resource failures. This paper investigates all of them to achieve a complete picture about grid service reliability. Markov models, queuing theory, and graph theory are mainly used to model, evaluate, and analyze the grid service reliability. Numerical examples are illustrated

Multiple sequence alignment is an important and difficult problem in molecular biology and bioinformatics. In this paper, we propose a partitioning approach that significantly improves the solution time and quality by utilizing the locality structure of the problem. The algorithm solves the multiple sequence alignment in three stages. First, an automated and suboptimal partitioning strategy is used to divide the set of sequences into several subsections. Then a multiple sequence alignment algorithm based on ant colony optimization is used to align the sequences of each subsection. Finally, the alignment of original sequences can be obtained by assembling the result of each subsection. The ant colony algorithm is highly optimized in order to avoid local optimal traps and converge to global optimal efficiently. Experimental results show that the algorithm can significantly reduce the running time and improve the solution quality on large-scale multiple sequence alignment benchmarks.

Protein sequence motifs information is very important to the analysis of biologically significant regions. The conserved regions have the potential to determine the conformation, function and activities of the proteins. The main purpose of this paper is trying to obtain protein sequence motifs which are universally conserved and across protein family boundaries. Therefore, unlike most popular motif discovering algorithms, our input dataset is extremely large. As a result, an efficient technique is demanded. In this article, short recurring segments of proteins are explored by utilizing a novel granular computing strategy. First, fuzzy C-means clustering algorithm (FCM) is used to separate the whole dataset into several smaller informational granules and then succeeded by improved K-means clustering algorithm on each granule to obtain the final results. The structural similarity of the clusters discovered by our approach is studied to analyze how the recurring patterns correlate with its structure. Also, some biochemical references are included in our evaluation. To the best of our knowledge, it is the first time that the granular computing concept as well as the DBI measure for evaluation is introduced to this dataset. Compare with the latest research results, our method requires only twenty percent of the execution time and obtains even higher quality information of protein sequence motifs. The efficient and satisfactory results in our experiment suggests that our granular computing model which combined FCM and improved K-means may have a high chance to be applied in some other bioinformatics research fields and yield stunning results

Modeling peer-to-peer (P2P) networks is a challenge for P2P researchers. In this paper, we provide a detailed analysis of large-scale hybrid P2P network topology, using Gnutella as a case study. First, we re-examine the power-law distributions of the Gnutella network discovered by previous researchers. Our results show that the current Gnutella network deviates from the earlier power-laws, suggesting that the Gnutella network topology may have evolved a lot over time. Second, we identify important trends with regard to the evolution of the Gnutella network between September 2005 and February 2006. Third, we provide a novel two-layered approach to study the topology of the Gnutella network. Due to the limitations of the power-laws, we divide the Gnutella network into two layers, namely the mesh and the forest, to model the hybrid and highly dynamic architecture of the current Gnutella network. We give a detailed analysis of the topology of the mesh and present two power-laws concerning the mesh topology. Moreover, we examine the topology properties of the forest and provide one empirical law concerning the tree size. Using the two-layered approach and laws proposed, we can generate realistic topologies easily.

In this paper we define the concepts of knowledge dependency, partial knowledge dependency and dependency degree in rough set models based on tolerance relations for incomplete information system. These concepts, not properly defined and studied in previous research study for incomplete information system, but are very important from data mining and rough set theory community. We consider two kinds of dependency relationships: one is related to the conditional attribute subset and the decision, and the other is related to two different conditional attribute subsets. We then propose several theorems based on our revised knowledge dependency relationships. We give formal proofs of the theorems and verify their correctness with some examples.

BACKGROUND: The problem of inferring the evolutionary history and constructing the phylogenetic tree with high performance has become one of the major problems in computational biology. RESULTS: A new phylogenetic tree construction method from a given set of objects (proteins, species, etc.) is presented. As an extension of ant colony optimization, this method proposes an adaptive phylogenetic clustering algorithm based on a digraph to find a tree structure that defines the ancestral relationships among the given objects. CONCLUSION: Our phylogenetic tree construction method is tested to compare its results with that of the genetic algorithm (GA). Experimental results show that our algorithm converges much faster and also achieves higher quality than GA.

BACKGROUND: Ant colony algorithm has emerged recently as a new meta-heuristic method, which is inspired from the behaviours of real ants for solving NP-hard problems. However, the classical ant colony algorithm also has its defects of stagnation and premature. This paper aims at remedying these problems. RESULTS: In this paper, we propose an adaptive ant colony algorithm that simulates the behaviour of biological immune system. The solutions of the problem are much more diversified than traditional ant colony algorithms. CONCLUSION: The proposed method for improving the performance of traditional ant colony algorithm takes into account the polarization of the colonies, and adaptively adjusts the distribution of the solutions obtained by the ants. This makes the solutions more diverse so as to avoid the stagnation and premature phenomena.

This paper first surveys security vulnerabilities of IEEE 802.11 WLANs. Then, two approaches to enhance security are proposed to overcome some known vulnerabilities and thus to provide better data confidentiality and authentication. Finally, simulation methodology is presented, and simulate results are provided. Our studies show that the proposed enhancements provide better data confidentiality with some degree of computing cost as the tradeoff.

The inherent uncertainty in wireless mobile ad hoc networks (MANET), due to nodal mobility, unstable links, and limited resources, frequently renders routing paths unusable. Thus, recurrent route discoveries detrimentally affect network performance. The most promising solution is to use multiple redundant paths for routing. However, selecting an optimal path set is a NP hard problem. Most current multi-path routing protocols do not concentrate on the uncertainty in MANET. They choose an "optimal" multi-path set by considering only one single route selection parameter, such as the least number of intermediate hops or the maximal remaining battery power. As a result, they miss the correlations among the multiple route selection parameters. This paper proposes the genetic fuzzy multi-path routing protocol (GFMRP), which is a multi-path routing protocol based on fuzzy set theory and evolutionary computing. GFMRP naturally deals with the uncertainty in MANET and adaptively constructs a set of highly reliable paths by considering the interplays among multiple route selection parameters. GFMRP takes into account four important factors as the selection parameters; which are the energy consumption rate, queue occupancy rate, link stability, and the number of intermediate nodes. The performance of GFMRP is evaluated in terms of packet delivery ratio, average end-to-end delay, and the frequency of route rediscovery in ns2 context. Simulation results demonstrate that GFMRP is well suited to the ad hoc environment and outperforms DSR, SMR and SBMR.

This paper presents the exchanged hypercube, a new interconnection network obtained by systematically removing links from a binary hypercube. It maintains several desirable properties of the binary hypercube yet with reduced interconnection complexity. We also introduce the extended binomial tree, a spanning tree of the exchanged hypercube that preserves many desirable properties of the original binomial tree. A fault-tolerant routing strategy is also proposed for the exchanged hypercube.

Nodes having a self-centrically broadcasting nature of communication form a wireless ad hoc network. Many issues are involved to provide quality of service (QoS) for ad hoc networks, including routing, medium access, resource reservation, mobility management, etc. Previous work mostly focuses on QoS routing with an assumption that the medium access control (MAC) layer can support QoS very well. However, contention-based MAC protocols are adopted in most ad hoc networks since there is no centralized control. QoS support in contention-based MAC layer is a very challenging issue. Carefully designed distributed medium access techniques must be used as foundations for most ad hoc networks. In this paper, we study and enhance distributed medium access techniques for real-time transmissions in the IEEE 802.11 single-hop ad hoc wireless networks. In the IEEE 802.11 MAC, error control adopts positive acknowledgement and retransmission to improve transmission reliability in the wireless medium (WM). However, for real-time multimedia traffic with sensitive delay requirements, retransmitted frames may be too late to be useful due to the fact that the delay of competing the WM is unpredictable. In this paper, we address several MAC issues and QoS issues for delay-sensitive real-time traffic. First, a priority scheme is proposed to differentiate the delay sensitive real-time traffic from the best-effort traffic. In the proposed priority scheme, retransmission is not used for the real-time traffic, and a smaller backoff window size is adopted. Second, we propose several schemes to guarantee QoS requirements. The first scheme is to guarantee frame-dropping probability for the real-time traffic. The second scheme is to guarantee throughput and delay. The last scheme is to guarantee throughput, delay, and frame-dropping probability simultaneously. Finally, we propose adaptive window backoff schemes to optimize throughput with and without QoS constraints.

Adaptive multimedia services are very attractive since resources in wireless/mobile networks are relatively scarce and widely variable, and more importantly the resource fluctuation caused by mobility and channel fading can be mitigated using adaptive services. Therefore, there are extensive research activities on Quality of Service (QoS), call admission control, as well as bandwidth degradation and adaptation for adaptive multimedia services in wireless/mobile networks in recent years. However, fairness of bandwidth degradation has largely been ignored in previous work and remains an important issue in adaptive multimedia service provisioning. In this paper, we propose and study proportional degradation service provisioning in wireless/mobile networks that offer multiple classes of adaptive multimedia services. The proposed proportional degradation fairness model guarantees the proportional bandwidth degradation among different classes of services. Two proportional degradation scenarios are studied in this paper. In the first scenario, we study the proportional degradation provisioning with two QoS parameters for adaptive multimedia: the degradation ratio (DR) and the degradation degree (DD). In the second scenario, we study the proportional degradation provisioning with a new QoS parameter for adaptive multimedia: the degradation area (DA). For each scenario, based on the QoS parameters, proportional degradation adaptation algorithms are proposed to approximate the proportional degradation model, to fairly adapt calls' degradations, to utilize the system resource efficiently, as well as to optimize QoS parameters. Performance studies show that in the first scenario, proportional DR has been achieved very well, whereas proportional DD has not been well achieved. In other words, DR outperforms DD in terms of proportional degradation. In the second scenario, proportional DA has been well achieved. Furthermore, bandwidth resources have been efficiently utilized and DA has been minimized. Copyright © 2004 John Wiley & Sons, Ltd.

Integration of 3G and wireless LAN (WLAN) becomes a trend in current and future wireless networks, and brings many benefits to both end users and service providers. In this paper, we provide a comprehensive survey on integration of 3G and WLAN. We discuss issues such as underline network architectures, integrated architectures, mobility management, and quality of service (QoS). We particularly study handoff QoS mapping and guarantee between 3G and WLAN, as well as how seamless voice/multimedia/data handoff becomes possible. Copyright © 2005 John Wiley & Sons, Ltd.

A new prediction scheme of transmembrane segments (TM) was developed based on the support vector machine (SVM). To apply this SVM for prediction more efficiently, three optimization processes were performed: encoding scheme, sliding window size and parameter optimization. From the encoding scheme optimization, position-specific scoring matrix (PSSM) encoding scheme is proved to be the most informative one and the prediction accuracy (Q/sub 2/) with this scheme attained up to 92%. Based on the performance comparison with previous studies, this PSSM encoding scheme demonstrates the highest prediction accuracy among the common prediction methods, and the accuracy improvement is more than 13%. To verify this scheme, the blind test was done with E.coli SecE and E.coli SecY transmembrane proteins, and the result shows a decent match with the SwissProt database information and the TopPred results. However, another blind test result with five SecA proteins leaves room for discussion since it shows about 8-9 residues long TM segments for all five proteins.

Protein secondary structure prediction is one of the most important problems in bioinformatics research. When the traditional tertiary classifier is used in our neural network, 72% accuracy is reached. Since the neural network might not work very well in three-class classification for certain domains, the three-class problem is reduced to six binary class problems for the first time to carry out protein secondary structure prediction. With the combination of six binary classifiers, we experiment and test several tertiary classifiers. Additionally, three new tertiary classifiers are proposed in this study: MAX/spl I.bar/HEC, ONE/spl I.bar/TO/spl I.bar/ONE/spl I.bar/MAX and ONE/spl I.bar/TO/spl I.bar/ONE/spl I.bar/VOTE. ONE/spl I.bar/TO/spl I.bar/ONE/spl I.bar/VOTE outperforms the six other experimental tertiary classifiers in this study. ONE/spl I.bar/TO/spl I.bar/ONE/spl I.bar/VOTE tertiary classifier with PSSM encoding scheme obtains 74.02% test accuracy on RS126 dataset. To the best of our knowledge, this is the best result for RS126 dataset with the cross-validation method for neural network. The improvement of prediction accuracy indicates that decomposition of the multiclass problem into several binary class problems may be applied to other areas of computational biology in order to increase generalization power of neural networks.

In this paper, a dynamic HLR (home location register) scheme for location management in PCS (personal communications service) networks is presented. The proposed scheme provides a dynamic copy of mobile terminal location information in the nearest (current) HLR database. A modified table lookup procedure is also proposed for determining the current HLR easily. It allows the location registration and call delivery to be performed efficiently. An analytical model is developed for studying the performance of the proposed scheme. The performance study shows that the proposed scheme significantly reduces the system overhead for location management in PCS networks.

Summary form only given. A parallel algorithm for EDT transform on linear array with reconfigurable pipeline bus system (LARPBS) is presented. For an image with n/spl times/n pixels, the algorithm can complete the EDT transform in O(nlogn/(c(n)logd(n))) time using n.d(n).c(n) processors, where c(n) and d(n) are parameters satisfying 1/spl les/c(n)/spl les/n , and 1

Summary form only given. Ant colony optimization (ACO) technique can be successfully implemented to solve many combinatorial optimization problems. In this paper we use the ACO technique to route messages through an N/spl times/N optical multistage interconnection network (OMIN) allowing up to 'C' limited crosstalk's (conflicts between messages within a switch) where 'C' is a technology driven parameter and is always less than log/sub 2/N. Messages with switch conflicts satisfying the crosstalk constraint are allowed to pass in the same group, but if there is any link conflict, then messages are routed in a different group. The focus here is to minimize the number of passes required for routing allowing up to 'C' limited crosstalks in an N/spl times/N OMIN. In this paper we show how the ACO technique can be applied to the routing problem, and its performance is compared to that of the degree-descending algorithm using simulation techniques. Finally the lower bound estimate on the minimum number of passes required is calculated and compared to the results obtained using the two algorithms discussed. The results obtained show that the ACO technique performs better than the degree-descending algorithm and is quite close to optimal algorithms to the problem.

Summary form only given. In this paper, a new class of optical multistage interconnection network (MIN) architecture is presented, which is constructed utilizing a modularization approach rather than the traditional recursive or fixed exchange pattern methods. We show that it has the best application flexibility and provides multicast function without imposing significant negative impacts to the whole network. A new multicast pattern is also proposed here, which makes it practical and economical to apply amplification in space-division networks. Compared with existing multicast architectures, this new architecture with Dilated Benes PTP modules has better performance in terms of system SNR, the number of switch elements, and system attenuation in point-to-point connections.

The Euclidean distance transform (EDT) converts a binary image into one where each pixel has a value equal to its distance to the nearest foreground pixel. Two parallel algorithms for EDT on linear array with reconfigurable pipeline bus system (LARPBS) are presented. For an image with n × n pixels, the first algorithm can complete EDT in O[(log n log log n)/(log log log n)] time using n2 processors. The second algorithm can computethe EDT in O(log n log log n) time using n2/(log log n) processors.

Abstract An important factor for high‐speed optical communication is the availability of ultrafast and low‐noise photodetectors. Among the semiconductor photodetectors that are commonly used in today's long‐haul and metro‐area fiber‐optic systems, avalanche photodiodes (APDs) are often preferred over p‐i‐n photodiodes due to their internal gain, which significantly improves the receiver sensitivity and alleviates the need for optical pre‐amplification. Unfortunately, the random nature of the very process of carrier impact ionization, which generates the gain, is inherently noisy and results in fluctuations not only in the gain but also in the time response. Recently, a theory characterizing the autocorrelation function of APDs has been developed by us which incorporates the dead‐space effect, an effect that is very significant in thin, high‐performance APDs. The research extends the time‐domain analysis of the dead‐space multiplication model to compute the autocorrelation function of the APD impulse response. However, the computation requires a large amount of memory space and is very time consuming. In this research, we describe our experiences in parallelizing the code in MPI and OpenMP using CAPTools. Several array partitioning schemes and scheduling policies are implemented and tested. Our results show that the code is scalable up to 64 processors on a SGI Origin 2000 machine and has small average errors. Copyright © 2004 John Wiley & Sons, Ltd.

A parallel algorithm for Euclidean distance transform (EDT) on linear array with reconfigurable pipeline bus system (LARPBS) is presented. For an image with n/spl times/n pixels, the algorithm can complete EDT transform in O(n log n/c(n) log d(n)) time using n/spl middot/d(n)/spl middot/c(n) processors, where c(n) and d(n) are parameters satisfying 1/spl les/c(n)/spl les/n, and 1

Location management is a key issue in personal communication service networks to guarantee the mobile terminals to continuously receive services when moving from one place to another. We study two location management schemes, a dynamic movement-based scheme (DYNAMIC-3G) and a static scheme (STATIC-3G), for 3G cellular networks where home location registers, gateway location registers (GLRs), and visitor location registers form a three-level hierarchical mobility database structure. For both schemes, the cost functions are formulated analytically. We prove that there is an optimal movement threshold that minimizes the total cost function of DYNAMIC-3G and propose a binary search algorithm to find the optimal threshold. Furthermore, we present performance evaluation and comparison of the proposed schemes with the previous schemes in 2G cellular networks where the GLR is not present. Our studies validate the optimality of the DYNAMIC-3G scheme and show that the proposed schemes outperform the previous schemes, especially when the remote-local-cost ratio is high. The comparison results between DYNAMIC-3G and STATIC-3G indicate that DYNAMIC-3G should be adopted when the mobility rate is low, and STATIC-3G should be adopted otherwise. Furthermore, DYNAMIC-3G tends to perform better than STATIC-3G when the paging cost is high or the number of cells in a location area is large.

We propose a dynamic fractional movement-based location management scheme for 3G networks where home location registers (HLRs), gateway location registers (GLRs), and visitor location registers (VLRs) are formed into a three-level hierarchical mobility database structure. The cost functions of location updates and paging are formulated analytically. We prove analytically that there is an optimal fractional movement threshold that minimizes the total cost function. Our study shows that the proposed scheme outperforms the previous two-tier mobility database scheme, especially when the remote-local-cost ratio is high.

Abstract The time‐dependent Maxwell equations are one of the most important approaches to describing dynamic or wide‐band frequency electromagnetic phenomena. A sequential finite‐volume, characteristic‐based procedure for solving the time‐dependent, three‐dimensional Maxwell equations has been successfully implemented in Fortran before. Due to its need for a large memory space and high demand on CPU time, it is impossible to test the code for a large array. Hence, it is essential to implement the code on a parallel computing system. In this paper, we discuss an efficient and scalable parallelization of the sequential Fortran time‐dependent Maxwell equations solver using High Performance Fortran (HPF). The background to the project, the theory behind the efficiency being achieved, the parallelization methodologies employed and the experimental results obtained on the Cray T3E massively parallel computing system will be described in detail. Experimental runs show that the execution time is reduced drastically through parallel computing. The code is scalable up to 98 processors on the Cray T3E and has a performance similar to that of an MPI implementation. Based on the experimentation carried out in this research, we believe that a high‐level parallel programming language such as HPF is a fast, viable and economical approach to parallelizing many existing sequential codes which exhibit a lot of parallelism. Copyright © 2003 John Wiley & Sons, Ltd.

With the increase of teletraffic demands in mobile cellular system, hierarchical cellular systems (HCSs) have been adopted extensively for more efficient channel utilization and better GoS (Grade of Services). A practical issue related to HCS is to design a scheme for controlling and allocating call traffic to different layers. There are several strategies to deal with this problem, such as no call-overflow scheme, unidirectional call-overflow scheme and bidirectional call-overflow scheme. The objective of this paper is to investigate a bidirectional call-overflow scheme, based on the velocity of the mobile making the calls. To ensure that hand off calls are given higher priorities, it is assumed that guard channels are assigned in both macrocells and microcells. In order to evaluate the performance of the new scheme and compare the performance of several related schemes, two now models based on a one-dimensional Markov process are developed and analytical results are derived. Theoretical analysis and numerical evaluation show that the proposed scheme outperforms others in terms of average hew call blocking and hand off failure probability of the system. In addition, when the teletraffic to the HCS reaches a certain grade, the GoS is insensitive to the maximum velocity and the velocity threshold which is used to assign calls to different layers in our scheme.

A parallel granular neural network (GNN) is developed to speed up data mining and knowledge discovery process for credit card fraud detection. The entire system is parallelized on the Silicon Graphics Origin 2000, which is a shared memory multiprocessor system consisting of 24-CPU, 4G main memory, and 200 GB hard-drive. In simulations, the parallel fuzzy neural network running on a 24-processor system is trained in parallel using training data sets, and then the trained parallel fuzzy neural network discovers fuzzy rules for future prediction. A parallel learning algorithm is implemented in C. The data are extracted into a flat file from an SQL server database containing sample Visa Card transactions and then preprocessed for applying in fraud detection. The data are classified into three categories: first for training, second for prediction, and third for fraud detection. After learning from training data, the GNN is used to predict on a second set of data and later the third set of data is applied for fraud detection. GNN gives fewer average training errors with larger amount of past training data. The higher the fraud detection error is, the greater the possibility of that transaction being actually fraudulent.

The linear array with a reconfigurable pipelined bus system (LARPBS) is a newly introduced parallel computational model, where processors are connected by a reconfigurable optical bus. In this paper, we show that the selection problem can be solved on the LARPBS model deterministically in O((loglogN)/sup 2// log log log N) time. To our best knowledge, this is the best deterministic selection algorithm on any model with a reconfigurable optical bus.

The computation model on which the algorithms are developed is the reconfigurable array of processors with wider bus networks (abbreviated to RAPWBN). The main difference between the RAPWBN model and other existing reconfigurable parallel processing systems is that the bus width of each network is bounded within the range [2,[/spl radic/(N)]]. Such a strategy not only saves the silicon area of the chip as well as increases the computational power enormously, but the strategy also allows the execution speed of the proposed algorithms to be tuned by the bus bandwidth. To demonstrate the computational power of the RAPWBN, the channel-assignment problem is derived in this paper. For the channel-assignment problem with N pairs of components, we first design an O(T + [N//spl omega/]) time parallel algorithm using 2N processors with a 2N-row by 2N-column bus network, where the bus width of each bus network is /spl omega/-bit for 2 /spl les/ /spl omega/ /spl les/ [/spl radic/N] and T = [log/sub /spl omega//N] + 1. By tuning the bus bandwidth to the natural log N-bit and the extended N/sup 1/c/-bit (N/sup 1/c/ > log N) for any constant c and c /spl ges/ 1, two more results which run in O(log N/log log N) and O(1) time, respectively, are also derived. When compared to the algorithms proposed by Olariu et al. [17] and Lin [14], it is shown that our algorithm runs in the equivalent time complexity while significantly reducing the number of processors to O(N).

Computing a distance map (distance transform) is an operation that converts a 2D image consisting of black and white pixels to an image where each pixel has a value or a pair of coordinates that represents the distance to or location of the nearest black pixel. It is a basic operation in image processing and computer vision fields, and is used for expanding, shrinking, thinning, segmentation, clustering, computing shape, object reconstruction, etc. This paper examines the possibility of implementing the problem of finding a distance map for an image efficiently using an optical bus. The computational model considered is the linear array with a reconfigurable pipelined bus system (LARPBS), which has been introduced recently based on current electronic and optical technologies. It is shown that the problem for an n /spl times/ n image can be implemented in O(log n log log n) bus cycles deterministically or in O(log n) bus cycles with high probability on an LARPBS with n/sup 2/ processors. We also show that the problem can be solved in O(log log n) bus cycles deterministically or in O(l) bus cycles with high probability on an LARPBS with n/sup 3/ processors. Scalability of the algorithms is also discussed briefly. The algorithm compares favorably to the best known parallel algorithms for the same problem in the literature.

This paper presents a dynamic database management method for location management of personal communications service (PCS) networks. The proposed method provides dynamics copies of user location information in the nearest home location register (HLR) database, which allows mobile users to access the system efficiently.

Wavelet transforms have proven to be useful tools for several applications, including signal analysis, signal coding, and image compression. In this paper, faster parallel algorithms for computing the continuous wavelet transform are designed for reconfigurable meshes. An -time algorithm for computing the continuous wavelet transform with signals and an integer grid on a 3-D reconfigurable mesh is proposed, where is the number of bits used to represent the values in calculation. A constant-time algorithm 3-D reconfigurable mesh is also proposed. To the best knowledge of the author, this is the first constanttime algorithm for continuous wavelet transform on any parallel architecture.

Because signals carried by two waveguides entering a common switch element would generate crosstalk, a regular N/spl times/N multistage interconnection network (MIN) cannot be directly used as an optical switch between N inputs and N outputs in an optical network. A simple solution is to use a 2N/spl times/2N cube-type MIN to provide the N/spl times/N connections, which needs a much larger hardware cost. A previous research proposed another solution, called the time-domain approach, that divides the N optical inputs into several groups such that crosstalk-free connections can be provided by an N/spl times/N regular MIN in several time slots, one for each group. Researchers studied this approach on Omega networks and defined the class set /spl theta/ to be the set of N-permutations realizable in two time slots on an Omega network. They proved that the size of /spl theta/ is larger than the size of class /spl Omega/, where /spl Omega/ consists of all N-permutations admissible to a regular N/spl times/N (nonoptical) Omega network. This paper first presents an optimal O(NlogN) time algorithm for identifying whether a given permutation belongs to class /spl theta/ or not. Using this algorithm, this paper then proves an interesting result that the class /spl theta/ is identical to the class /spl Omega/+1 which represents the set of N-permutations admissible to a nonoptical N/spl times/N one-extra stage Omega network.

Mesh-connected computers (MCCs) are a class of important parallel architectures due to their simple and regular interconnections. However, their performances are restricted by their large diameters. Various augmenting mechanisms have been proposed to enhance the communication efficiency of MCCs. One major approach is to add nonconfigurable buses for improved broadcasting. A typical example is the mesh-connected computer with multiple buses (MMB). We propose a new class of generalized MMBs, the improved generalized MMBs (IMMBs). We compare IMMBs with MMBs and a class of previously proposed generalized MMBs (GMMBs). We show the power of IMMBs by considering semigroup and prefix computations. Specifically, as our main result we show that for any constant 0½×N½square IMMB using which semigroup and prefix computations on N operands can be carried out in O(Nε) time, while maintaining O(1) broadcasting time. Compared with the previous best complexities O(N⅛) and O(N1/16) achieved on a rectangular MMB and GMMB, respectively, for the same computations, our results show that IMMBs are more powerful than MMBs and GMMBs.

The Block Shift Network (BSN) is a new topology for interconnection networks in multiprocessor systems. BSN is a class of networks defined by several parameters, and has a constant number of links/node for some given parameters. Many popular networks such as the hypercube, the shuffle-exchange, and the complete networks, are instances of the BSN for different parameters. Performance of BSN has been evaluated through analysis, simulation, and design of typical parallel algorithms on it. The results indicate that BSN surpasses the hypercube in several respects while retaining most of the hypercube advantages, especially when the traffic has the locality property. As the size and complexity of a system increase, however, the reliability aspects become equally important and should be included in the system-performance study. This paper discusses the reliability issue of BSN. Several reliability measures, including network connectivity, network diagnosability, and 2-terminal reliability, are obtained through analysis. This paper shows that the BSN not only surpasses the hypercube in performance as confirmed before, but also has comparable reliability to the hypercube under similar conditions. BSN is also very flexible in balancing its cost and performance. One can increase two parameters to enhance the performance and reliability of the BSN, while it is impossible to do so in the hypercube once its size is fixed. The BSN can be an effective interconnection network for future parallel computer systems.

Various augmenting mechanisms have been proposed to enhance the communication efficiency of mesh-connected computers (MCCs). One major approach is to add nonconfigurable buses for improved broadcasting. A typical example is the mesh-connected computer with multiple buses (MMB). In this paper, we propose a new class of generalized MMBs, the improved generalized MMBs (IMMBs). Each processor in an IMMB is connected to exactly two buses. We show the power of IMMBs by considering semigroup and prefix computations. Specifically, we show that semigroup and prefix computations on N operands, and data broadcasting all take O(log N) time on IMMBs. This is the first O(log N) time algorithm for these problems on arrays with fixed broadcasting buses.

Given a set of precedence constrained parallel tasks with their processor requirements and execution times, the problem of scheduling precedence constrained parallel tasks on multicomputers with contiguous processor allocation is to find a nonpreemptive schedule of the tasks on a multicomputer such that the schedule length is minimized. This scheduling problem is substantially more difficult than other scheduling problems due to precedence constraints among tasks, the inherent difficulty of task scheduling, and processor allocation in multicomputers. We present an approximation algorithm called LLB that schedules tasks level-by-level using the largest-task-first strategy supported by the binary system partitioning scheme to handle the three difficult issues in our scheduling problem. Though algorithm LLB does not have a bounded worst-case performance ratio, we show through probabilistic analysis that LLB has a quite reasonable average-case performance ratio for typical classes of parallel computations. In particular, algorithm LLB has an average-case performance ratio less than two for large scale parallel computations that have wide task graphs (i.e., that exhibit large parallelism).

The distance calculation in an image is a basic operation in computer vision, pattern recognition, and robotics. Several parallel algorithms have been proposed for calculating the Euclidean distance transform (EDT). Recently, Chen and Chuang proposed a parallel algorithm for computing the EDT on mesh-connected SIMD computers (1995). For an nxn image, their algorithm runs in O(n) time on a two-dimensional (2-D) nxn mesh-connected processor array. In this paper, we propose a more efficient parallel algorithm for computing the EDT on a reconfigurable mesh model. For the same problem, our algorithm runs in O(log(2)n) time on a 2-D nxn reconfigurable mesh. Since a reconfigurable mesh uses the same amount of VLSI area as a plain mesh of the same size does when implemented in VLSI, our algorithm improves the result in [3] significantly.

Since optical networks must support crosstalk-free (CF) connections, regular N/spl times/N multistage interconnection networks (MINs) cannot be directly used to provide connections between N inputs and N outputs. A solution is to use a 2N/spl times/2N Cube-Type MIN to provide the N/spl times/N connections. C. Qiao (1993) proposed a time domain approach that divides the N optical inputs into several groups such that CF connections can be provided by an N/spl times/N regular MIN in several time slots, one for each group. The set of N-permutations realizable in two slots is defined as class /spl theta/. Qiao proved that the size of /spl theta/ is larger than the size of class /spl Omega/, where /spl Omega/ consists of all N-permutations passible through a regular N/spl times/N Omega network. The paper first presents an optimal O(NlgN) time algorithm for identifying whether a given permutation belongs to class /spl theta/ or not. From this algorithm, the paper proves an interesting fact that the class /spl theta/ is identical to the class /spl Omega/+1 which represents the set of N-permutations admissible by a non-optical N/spl times/N one-extra stage Omega network.

This paper addresses the problem of efficient routing in unreliable multihop optical networks supported by Wavelength Division Multiplexing (WDM). We first define a new cost model for routing in (optical) WDM networks that is more general than the existing models. Our model takes into consideration not only the cost of wavelength access and conversion but also the delay for queuing signals arriving at different input channels that share the same output channel at the same node. We then propose a set of efficient algorithms in a reliable WDM network on the new cost model for each of the three most important communication patterns-multiple point-to-point routing, multicast, and multiple multicast. Finally, we show how to obtain a set of efficient algorithms in an unreliable WDM network with up to f faulty optical channels and wavelength conversion gates. Our strategy is to first enhance the physical paths constructed by the algorithms for reliable networks to ensure success of fault-tolerant routing, and then to route among the enhanced paths to establish a set of fault-free physical routes to complete the corresponding routing request for each of the communication patterns.

The Hough transform is an important problem in image processing and computer vision. An efficient algorithm for computing the Hough transform has been proposed on a reconfigurable array by Kao et al. (1995). For a problem with an /spl radic/N/spl times//spl radic/N image and an n/spl times/n parameter space, the algorithm runs in a constant time on a three-dimensional (3-D) n/spl times/n/spl times/N reconfigurable mesh where the data bus is N/sup 1/c/-bit wide. To our best knowledge, this is the most efficient constant-time algorithm for computing the Hough transform on a reconfigurable mesh. In this paper, an improved Hough transform algorithm on a reconfigurable mesh is proposed. For the same problem, our algorithm runs in constant time on a 3-D n*n/spl times/n/spl times//spl radic/n/spl radic/n reconfigurable mesh, where the data bus is only log N-bit wide. In most practical situations, n=O(/spl radic/N). Hence, our algorithm requires much less VLSI area to accomplish the same task. In addition, our algorithm can compute the Radon transform (a generalized Hough transform) in O(1) time on the same model, whereas the algorithm in the above paper cannot be adapted to computing Radon transform easily.

We present efficient parallel matrix multiplication algorithms for linear arrays with reconfigurable pipelined bus systems (LARPBS). Such systems are able to support a large volume of parallel communication of various patterns in constant time. An LARPBS can also be reconfigured into many independent subsystems and, thus, is able to support parallel implementations of divide-and-conquer computations like Strassen's algorithm. The main contributions of the paper are as follows. We develop five matrix multiplication algorithms with varying degrees of parallelism on the LARPBS computing model; namely, MM/sub 1/, MM/sub 2/, MM/sub 3/, and compound algorithms C/sub 1/(/spl epsiv/)and C/sub 2/(/spl delta/). Algorithm C/sub 1/(/spl epsiv/) has adjustable time complexity in sublinear level. Algorithm C/sub 2/(/spl delta/) implies that it is feasible to achieve sublogarithmic time using /spl sigma/(N/sup 3/) processors for matrix multiplication on a realistic system. Algorithms MM/sub 3/, C/sub 1/(/spl epsiv/), and C/sub 2/(/spl delta/) all have o(/spl Nscr//sup 3/) cost and, hence, are very processor efficient. Algorithms MM/sub 1/, MM/sub 3/, and C/sub 1/(/spl epsiv/) are general-purpose matrix multiplication algorithms, where the array elements are in any ring. Algorithms MM/sub 2/ and C/sub 2/(/spl delta/) are applicable to array elements that are integers of bounded magnitude, or floating-point values of bounded precision and magnitude, or Boolean values. Extension of algorithms MM/sub 2/ and C/sub 2/(/spl delta/) to unbounded integers and reals are also discussed.

Parallel computing on clusters of workstations is receiving much attention from the research community. Unfortunately, many aspects of parallel computing over this parallel computing engine is not very well understood. Some of these issues include the workstation architectures, the network protocols, the communication-to-computation ratio, the load balancing strategies, and the data partitioning schemes. The aim of this paper is to assess the strengths and limitations of a cluster of workstations by capturing the effects of the above issues. This has been achieved by evaluating the performance of this computing environment in the execution of a parallel ray tracing application through analytical modeling and extensive experimentation.

The distance transform is a basic operation in computer vision, pattern recognition and robotics. In this paper, we consider the city block (L1) distance metric. An algorithm for computing the city block distance transform on reconfigurable meshes is proposed in this paper. The time complexity and scalability of the algorithm are analysed. The results indicate that the algorithm is scalable and efficient.

Randomized selection algorithms on k-dimensional mesh-connected computers with multiple broad-casting are proposed in this paper. We first show that a leader can be elected in O(log N) time on any k-dimensional mesh-connected computers with multiple broadcasting of size N. We then show that we can find the p-th smallest element among a data set of size N in O((logN + k + N1/k(k + 1))) log N) expected time using a regular N1/k ×…×N1/kk-dimensional mesh and in O((log N + k2N1/k2k)) log N) expected time using an irregular N(2k-1k + 1)/(k2k)×N(2k-2k + 1)/(k2k×…×N(k + 1)/(k2kk-dimensional mesh. This leads to a selection algorithm which runs in O((log N)2) expected time on a regular ((log N/log log N)1/2)-dimensional mesh or on an irregular (log log N)-dimensional mesh each with N processors. To our best knowledge, this is the first polylogarithmic selection algorithm on meshes with multiple broadcasting.