Chromatin loops serve as fundamental functional units of three-dimensional genome organization, playing pivotal roles in regulating gene expression and maintaining genomic spatial organization. Accurate identification of these fine-scale structures is crucial for advancing our understanding of cellular biological processes and the mechanisms underlying disease. However, due to the inherent complexity and dynamic of chromatin interactions, existing methods often fail to adequately characterize and capture multi-dimensional features. To address these limitations, we introduce GraphLooper, a novel framework using hierarchical multi-view graph pooling to enhance training and inference on large-scale data. GraphLooper transforms Hi-C data into a graph-structured representation, integrating multi-dimensional epigenomic features to construct a robust chromatin interaction model. Employing a hierarchical multi-view graph pooling mechanism, it effectively aggregates multi-scale features, enhancing representation learning. Evaluations across diverse cell lines demonstrate that GraphLooper outperforms state-of-the-art methods in prediction accuracy and generalization, particularly in capturing long-range chromatin interactions critical for precise spatial gene regulation.

Spatial transcriptomics (ST) technologies provide powerful tools for analyzing spatial distribution patterns of gene expression in tissue samples. However, they are limited by sparse gene detection and incomplete expression coverage. Several computational approaches based on reference scRNA-seq have been proposed to impute ST data and have achieved impressive results. However, these methods fail to fully explore latent temporal dependencies among cells and cannot accurately capture hidden gene-level regulatory mechanisms. To overcome those limitations, we propose SpaLSTF, a novel method for enhancing ST gene expression using a conditional diffusion model guided by scRNA-seq data. SpaLSTF captures gene expression relationships through a dual Markov process: one progressively perturbs scRNA-seq data with noise, while the other denoises it to reconstruct the original distribution. To effectively model contextual dependencies among cell states, we adopt a bidirectional long short-term memory (BiLSTM) network. Furthermore, we design a cross-covariance attention mechanism within a Transformer (XCA-Transformer) to efficiently compute attention coefficients between gene expression and accurately predict the noise added at each step. In addition, we introduce a variational lower bound (VLB) objective and introduce Kullback-Leibler (KL) divergence as a regularization term, along with mean squared error loss, to ensure that the generated noise follows the target distribution. We compared the performance of SpaLSTF with seven state-of-the-art methods on twelve cross-platform datasets covering a variety of tissues and organs using nine evaluation metrics. Experimental results demonstrated that SpaLSTF outperforms competing methods in gene expression imputation, cell population identification, and spatial structure preservation.

[This corrects the article DOI: 10.1371/journal.pcbi.1013667.].

Tokenized pose estimation (TPE) has demonstrated remarkable performance in lightweight human pose estimation (HPE) models. However, existing TPE methods typically initialize keypoint tokens randomly, without explicitly incorporating human structure priors. These priors play a vital role in HPE by effectively mitigating common challenges such as occlusion and ambiguity. To this end, we propose a Structure-Aware Keypoint Position Embedding (SAKPE). This embedding explicitly encodes inherent structural properties of the human body, such as symmetry and order, into the positional coordinates of keypoint tokens. It also employs learnable scale and offset factors to adapt to diverse human poses, thereby fully exploiting the geometric constraints among keypoints. Furthermore, to better leverage the positional relationships among patch tokens, we introduce a Layer-adaptive Hybrid Patch Position Embedding (LHPPE). It dynamically fuses absolute and relative position embeddings of patch tokens based on attention distributions across Transformer layers, enabling the model to learn both absolute and relative positional information adaptively. Taking the two together, we propose a novel position embedding method for pose estimation, named Human-structure-aware Token Position Embedding (HTPE). It significantly improves the performance of various TPE models. Extensive experiments on COCO, CrowdPose, and OCHuman show that HTPE achieves state-of-the-art (SOTA) performance among lightweight methods, with a negligible increase in parameters and FLOPs. Notably, it demonstrates consistent improvements under occlusion,, achieving up to 3.3 AP gains. The source code can be found in https://github.com/guzejungithub/HTPE.

In practical applications of human pose estimation, low-resolution inputs frequently occur, and existing state-of-the-art models perform poorly with low-resolution images. This work focuses on boosting the performance of low-resolution models by distilling knowledge from a high-resolution model. However, we face the challenge of feature size mismatch and class number mismatch when applying knowledge distillation to networks with different input resolutions. To address this issue, we propose a novel cross-domain knowledge distillation (CDKD) framework. In this framework, we construct a scale-adaptive projector ensemble (SAPE) module to spatially align feature maps between models of varying input resolutions. It adopts a projector ensemble to map low-resolution features into multiple common spaces and adaptively merges them based on multi-scale information to match high-resolution features. Additionally, we construct a cross-class alignment (CCA) module to solve the problem of the mismatch of class numbers. By combining an easy-to-hard training (ETHT) strategy, the CCA module further enhances the distillation performance. The effectiveness and efficiency of our approach are demonstrated by extensive experiments on three common benchmark datasets: MPII, COCO, and Crowdpose.

Recently, Graph neural networks(GNNs) has achieved tremendous success in a variety of fields. Many approaches have been proposed to address data with graph structure. However, many of these are deterministic methods, therefore, they are unable to capture the uncertainty, which is inherent in the nature of graph data. Various VAE(Variational auto-encoder)-based approaches have been proposed to tackle such problems. Unfortunately, due to the simple a posterior and a prior assumption problems of such methods, they are not well suited to handle uncertainty in graph data. For example, VGAE(Variational graph auto-encoder) assumes that the posterior and prior distributions are simple Gaussian distributions, which can lead to overfitting problems when incompatible with the true distributions. Many methods propose to solve the posterior distribution problem, but most ignore the effect of the prior distribution. Therefore, in this paper, we proposed a novel method to solve the Gaussian prior problem. Specifically, in order to enhance the representation power of the prior distribution, we use the diffusion model to model the prior distribution. We incorporate the diffusion model into VGAE. In the forward diffusion process, noise is gradually added to the latent variables, and then the samples are recovered by the backward diffusion process. To realize the backward diffusion process, we propose a new denoising model which predicts noise by stacking GCN(Graph Convolution Network) and MLP(Multi-layers Perceptron). We perform experiments on different datasets and the experimental results demonstrate that our method obtains state-of-the-art results.

Single-cell RNA sequencing (scRNA-seq) technology has opened new frontiers in biomedical research, offering insights into cellular heterogeneity. Accurate cell clustering and batch effect correction are essential in single-cell RNA sequencing (scRNA-seq) data analysis, forming the foundation for downstream steps. However, most methods handle these tasks separately, limiting their applicability across diverse datasets. To address these challenges, we introduce Relational Graph Convolutional Network with Batch Awareness (RGCN-BA), a deep learning framework that integrates cell clustering and batch effect correction into a unified model. For multi-batch datasets, RGCN-BA leverages relational graph convolutional network to process batch information as distinct edge types, followed by a batch correction layer for global alignment. For single-batch data, it functions with a single edge type. Experiments on both multi-batch and single-batch datasets demonstrate that RGCN-BA outperforms both specialized clustering methods and batch effect correction methods. This versatility in handling both tasks positions RGCN-BA as a powerful tool for enhancing scRNA-seq data analysis.

Cell type annotation is a critical step in analyzing single-cell RNA sequencing (scRNA-seq) data. A large number of deep learning (DL)-based methods have been proposed to annotate cell types of scRNA-seq data and have achieved impressive results. However, there are several limitations to these methods. First, they do not fully exploit cell-to-cell differential features. Second, they are developed based on shallow features and lack of flexibility in integrating high-order features in the data. Finally, the low-dimensional gene features may lead to overfitting in neural networks. To overcome those limitations, we propose a novel DL-based model, cell type annotation of single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning (scRGCL), based on residual graph convolutional neural network and contrastive learning for cell type annotation of single-cell RNA-seq data. scRGCL mainly consists of a residual graph convolutional neural network, contrastive learning, and weight freezing. A residual graph convolutional neural network is utilized to extract complex high-order features from data. Contrastive learning can help the model learn meaningful cell-to-cell differential features. Weight freezing can avoid overfitting and help the model discover the impact of specific gene expression on cell type annotation. To verify the effectiveness of scRGCL, we compared its performance with six methods (three shallow learning algorithms and three state-of-the-art DL-based methods) on eight single-cell benchmark datasets from two species (seven in human and one in mouse). Experimental results not only show that scRGCL outperforms competing methods but also demonstrate the generalizability of scRGCL for cell type annotation. scRGCL is available at https://github.com/nathanyl/scRGCL.

CircRNAs are a particular single-stranded, circular structure and “non-coding” RNA molecules, with various biological functions . Existing studies have demonstrated the fundamental role of circRNAs in gene expression regulation and their significant involvement in the development of diverse complex diseases. Predicting the protein binding sites in circRNA can aid in comprehending the regulation mechanism involved in circRNA-protein binding during gene expression and facilitate the investigation of potential diagnosis and treatment strategies for complex diseases. This review begins by introducing the concept and functions of circRNAs, as well as their involvement in gene expression regulation . Then, some critical and publicly accessible databases about circRNA annotation, protein annotation, circRNA-protein binding were listed. Next, we present a brief introduction to the computational model for predicting circRNA-protein binding, followed by model performance comparison and suggestions for non-computer science experts on model selection. Finally, we examine the problems, limitations, and advantages of computational models and explore the further direction of circRNA-protein prediction, such as developing new and complex computational models, introducing complex biological sequence encoding schemes, and integrating additional biological data related to circRNA-protein binding.

The rapid development of spatial transcriptomics (ST) has made it possible to effectively integrate gene expression and spatial information of cells and accurately identify spatial domains. A large number of deep learning (DL)-based methods have been proposed to perform spatial domain identification and achieved impressive results. However, these methods have some limitations. First, these methods rely on a fixed similarity metric and cannot fully utilize neighborhood information. Second, they cannot efficiently and adaptively integrate key information when fusing and reconstructing gene expression using purely additive methods. Finally, these methods ignore key nonlinear features and introduce noise during clustering. To address these limitations, we propose a novel DL model SpaMWGDA based on multi-view weighted fused graph convolutional network (GCN) and data augmentation. By modeling spatial information using different similarity metrics, the model is able to successfully capture comprehensive neighborhood information of the spot features. By combining data augmentation and contrastive learning, SpaMWGDA is able to learn key gene expressions. SpaMWGDA uses a multi-view GCN encoder to model the similarities between spatial information and gene features, and uses a view-level attention mechanism for weighted fusion to adaptively learn the dependencies between them and learn the key features of each view. Experimental results not only demonstrate that SpaMWGDA outperforms competing methods in spatial domain identification and trajectory inference but also show the ability of SpaMWGDA to analyse tissue structure and function. The source code for SpaMWGDA is available at https://github.com/nathanyl/SpaMWGDA .

Multimodal remote sensing semantic segmentation based on Optical and Digital Surface Model (Opt-DSM) data is pivotal for comprehensive scene interpretation. However, prevailing methodologies often lack a unified vision foundation model and encounter significant challenges in bridging modality gaps and achieving effective feature fusion. Conventional models, such as the Segment Anything Model (SAM), exhibit inherent limitations when addressing the unique complexities of multimodal remote sensing, particularly in managing cross-modal discrepancies and intricate surface structures. In this study, we present VF-MET (Vision Foundation Model-Driven Multi-Scale Expert Tuning), an innovative framework meticulously tailored for Opt-DSM semantic segmentation tasks. VF-MET incorporates an adaptive Multi-Scale Expert Tuning (AMET) strategy, which substantially enhances the feature extraction capabilities of vision foundation models. This enables the robust capture of cross-scale and morphologically irregular objects, while simultaneously preserving superior generalization ability. To further address the segmentation of densely distributed and weakly correlated regions, we propose a collaborative Box-Point Prompt Mechanism (CBPM), which significantly improves spatial localization and contextual discrimination. Moreover, we introduce a Two-Stage Mask Decoder (TSMD) that facilitates efficient multimodal feature fusion and augments contextual understanding. Extensive experiments conducted on public Opt-DSM benchmark datasets unequivocally demonstrate that VF-MET achieves state-of-the-art performance. Comprehensive ablation studies further substantiate the indispensable contributions of each constituent module within the proposed architecture. The source code and datasets are publicly accessible at https://github.com/NWPUFranklee/VF-MET.git.

Road extraction from remote sensing imagery is crucial for a variety of applications, including transportation monitoring, disaster response, and urban planning. However, existing methods often fail to accurately delineate sparse, curvilinear, and boundary-blurred road structures in high-resolution images, leading to incomplete detail preservation and inadequate contextual understanding. To address these challenges, we propose a novel Frequency-Driven Dual-Branch Mamba Network (FDMamba) for precise road extraction from remote sensing imagery. The proposed FDMamba integrates frequency-aware modeling with a dual-branch architecture, enabling collaborative learning of fine-grained edge details and global spatial dependencies. Specifically, FDMamba comprises three key modules: a Fourier Reconstruction Attention Mechanism (FRAM) to enhance high-frequency boundary information and low-frequency structural representation; a Rotation-aware Mamba Module (RAMamba) that leverages multi-path state space modeling for robust directional perception of road structures; and a Phase-guided Feature Fusion Module (PFFM) for effective cross-scale alignment and fusion of high- and low-frequency features. Furthermore, to mitigate the issue of blurred or ambiguous boundaries, we introduce a hybrid loss function that combines binary cross-entropy, focal loss, and frequency-aware loss, explicitly guiding the model to focus on edge structure and multi-frequency complementary information. Extensive experiments on three benchmark datasets, CHN6-CUG, DeepGlobe, and Massachusetts, demonstrate that FDMamba consistently outperforms state-of-the-art methods in terms of F1-score and IoU, achieving superior boundary clarity and structural continuity while preserving overall geometric integrity. The code is available at https://github.com/darkseid-arch/RE-FDMamba.

Benefiting from the powerful feature extraction and feature correlation modeling capabilities of convolutional neural networks (CNNs) and Transformer models, these techniques have been widely used in unmanned aerial vehicle (UAV) aerial image semantic segmentation tasks. However, the ground objects in aerial images contain feature information with different scales, and existing methods directly cascade low-level visual features and high-level semantic features without processing, resulting in low semantic segmentation precision. To address these challenges, we propose a dual-encoder cross-scale attention network, which efficiently extracts local and global context information from aerial images and performs fine-grained fusion of multiscale features to improve semantic segmentation performance. First, we introduce the dual-CNN-Transformer encoder, which embeds the scan-focus window Transformer (SFWT) into CNNs as an auxiliary encoder to supplement the local feature information lost in the global context information extraction process. Second, the cross-scale lightweight integration (CSLI) module is designed, which uses a light dot-product attention mechanism (DPAM) to fusion multiscale features and reduce model calculation parameters. Finally, the linear multilayer perceptron (LMLP) is used to restore the feature map resolution while expanding the deconvolution receptive field. To validate the effectiveness of the proposed method, we conducted extensive experiments on real aerial scene datasets, including UAVid, Urban Drone, and AeroScapes. The experimental results show that our method achieves state-of-the-art performance while maintaining superior real-time efficiency. Implementation codes will be available athttps://github.com/darkseid-arch/UAVSeg.

CircRNA-miRNA interaction (CMI) plays a crucial role in the gene regulatory network of the cell. Numerous experiments have shown that abnormalities in CMI can impact molecular functions and physiological processes, leading to the occurrence of specific diseases. Current computational models for predicting CMI typically focus on local molecular entity relationships, thereby neglecting inherent molecular attributes and global structural information. To address these limitations, we propose a multi-feature fusion prediction model based on the transformer and graph attention network, named EGATCMI. Specifically, EGATCMI combines the transformer architecture with Word2vec to pre-train the sequence of circRNA and miRNA, capturing their sequence feature representation and sequence similarity. By leveraging the self-attention mechanism, EGATCMI extracts global structural feature from the CMI network. EGATCMI effectively integrates the obtained multi-feature for prediction, achieving AUC values of 0.9106 and 0.9470 on the CMI-9905 and CircBank datasets, respectively, outperforming existing methods. In case studies that the prediction of interactions between three miRNAs that are closely related to diseases and circRNAs, 8 out of 10 pairs were accurately predicted and validated. Extensive experimental results demonstrate the potential of EGATCMI as a reliable tool for candidate screening in biological investigations.

Serial section electron microscopy (ssEM) is a pivotal technique for investigating neuronal connections and brain microstructures. However, imperfect sample preparation and image acquisition often lead to degradation, posing challenges for subsequent analysis. While previous deep learning methods, such as the interpolation model using spatially adaptive convolutions, have been proven to outperform conventional approaches, they struggle to recover high-frequency details, resulting in poor perceptual quality and segmentation performance. This study presents a novel approach leveraging diffusion models to restore missing slices of ssEM images. To accommodate the anisotropic characteristic of ssEM images, we enhance the backbone network with asymmetric and symmetric 3D convolutions. Additionally, we propose the Adaptive and Learnable Reconstruction (ALR) module with the First and Last slices Attention Block (FLAB) for effective feature extraction. A Multi-output Joint Strategy (MJS) is utilized for noise estimation, reducing training-testing discrepancies and achieving diffusion correction. Moreover, we also redesign the inference process to optimize the restoration of partially damaged slices, enabling restoration without additional artifact simulation or retraining. Experiment results demonstrate the effectiveness of our approach in generating more realistic slices and its superior performance in downstream tasks, surpassing previous methods.

The advent of single-cell sequencing technologies has revolutionized cell biology studies. However, integrative analyses of diverse single-cell data face serious challenges, including technological noise, sample heterogeneity, and different modalities and species. To address these problems, we propose scCorrector, a variational autoencoder-based model that can integrate single-cell data from different studies and map them into a common space. Specifically, we designed a Study Specific Adaptive Normalization for each study in decoder to implement these features. scCorrector substantially achieves competitive and robust performance compared with state-of-the-art methods and brings novel insights under various circumstances (e.g. various batches, multi-omics, cross-species, and development stages). In addition, the integration of single-cell data and spatial data makes it possible to transfer information between different studies, which greatly expand the narrow range of genes covered by MERFISH technology. In summary, scCorrector can efficiently integrate multi-study single-cell datasets, thereby providing broad opportunities to tackle challenges emerging from noisy resources.

The gene regulatory network (GRN) plays a vital role in understanding the structure and dynamics of cellular systems, revealing complex regulatory relationships, and exploring disease mechanisms. Recently, deep learning (DL)-based methods have been proposed to infer GRNs from single-cell transcriptomic data and achieved impressive performance. However, these methods do not fully utilize graph topological information and high-order neighbor information from multiple receptive fields. To overcome those limitations, we propose a novel model based on multiview graph attention network, namely, scMGATGRN, to infer GRNs. scMGATGRN mainly consists of GAT, multiview, and view-level attention mechanism. GAT can extract essential features of the gene regulatory network. The multiview model can simultaneously utilize local feature information and high-order neighbor feature information of nodes in the gene regulatory network. The view-level attention mechanism dynamically adjusts the relative importance of node embedding representations and efficiently aggregates node embedding representations from two views. To verify the effectiveness of scMGATGRN, we compared its performance with 10 methods (five shallow learning algorithms and five state-of-the-art DL-based methods) on seven benchmark single-cell RNA sequencing (scRNA-seq) datasets from five cell lines (two in human and three in mouse) with four different kinds of ground-truth networks. The experimental results not only show that scMGATGRN outperforms competing methods but also demonstrate the potential of this model in inferring GRNs. The code and data of scMGATGRN are made freely available on GitHub (https://github.com/nathanyl/scMGATGRN).

Circular RNAs (circRNAs) play vital roles in transcription and translation. Identification of circRNA-RBP (RNA-binding protein) interaction sites has become a fundamental step in molecular and cell biology. Deep learning (DL)-based methods have been proposed to predict circRNA-RBP interaction sites and achieved impressive identification performance. However, those methods cannot effectively capture long-distance dependencies, and cannot effectively utilize the interaction information of multiple features. To overcome those limitations, we propose a DL-based model iCRBP-LKHA using deep hybrid networks for identifying circRNA-RBP interaction sites. iCRBP-LKHA adopts five encoding schemes. Meanwhile, the neural network architecture, which consists of large kernel convolutional neural network (LKCNN), convolutional block attention module with one-dimensional convolution (CBAM-1D) and bidirectional gating recurrent unit (BiGRU), can explore local information, global context information and multiple features interaction information automatically. To verify the effectiveness of iCRBP-LKHA, we compared its performance with shallow learning algorithms on 37 circRNAs datasets and 37 circRNAs stringent datasets. And we compared its performance with state-of-the-art DL-based methods on 37 circRNAs datasets, 37 circRNAs stringent datasets and 31 linear RNAs datasets. The experimental results not only show that iCRBP-LKHA outperforms other competing methods, but also demonstrate the potential of this model in identifying other RNA-RBP interaction sites.

Accumulating evidence from recent research reveals that circRNA is tightly bound to human complex disease and plays an important regulatory role in disease progression. Identifying disease-associated circRNA occupies a key role in the research of disease pathogenesis. In this study, we propose a new model AMDECDA for predicting circRNA-disease association (CDA) by combining attention mechanism and data ensemble strategy. Firstly, we fuse the heterogeneous information including circRNA Gaussian interaction profile (GIP), disease semantics and disease GIP, and then use the attention mechanism of Graph Attention Network (GAT) to focus on the critical information of data, reasonably allocate resources and extract their essential features. Finally, the ensemble deep RVFL network (edRVFL) is utilized to quickly and accurately predict CDA in the non-iterative manner of closed-form solutions. In the five-fold cross-validation experiment on the benchmark data set, AMDECDA achieves an accuracy of 93.10% with a sensitivity of 97.56% in 0.9235 AUC. In comparison with previous models, AMDECDA exhibits highly competitiveness. Furthermore, 26 of the top 30 unknown CDAs of AMDECDA predicted scores are proved by the related literature. These results indicate that AMDECDA can effectively anticipate latent CDA and provide help for further biological wet experiments.

Researchers have been paying more attention to hyperspectral image (HSI) classification based on semi-supervised superpixel-level graph convolutional networks (SGCNs) due to their aggregation ability of rich contextual information. Although these SGCNs achieve good classification performance, the influence of the positional distributions among labeled superpixels has been overlooked. The locations of labeled superpixels, such as located at class boundaries or centers, exert a substantial influence on the final performance. To address this issue, this article proposed a novel graph neural network (GCN) method with the guidance of positional distributions of labeled superpixels, abbreviated as LPDGCN. Specifically, we first propose to utilize the sparse, low-rank as well as feature smoothness restrictions to optimize the initial superpixel graph structure because the connectivity relationships of labeled superpixels located at class boundaries or centers are easily influenced by spectral variation. Second, in order to effectively determine the positional distributions of labeled superpixels and make full use of the position relationships, we propose to utilize the information conflict from the above topology connectivity to determine the positional distributions of labeled superpixels and develop the reweighted strategy to weaken the influence of labeled superpixels located at class boundaries and strengthen the influence of that located at class centers. Finally, we evaluate the LPDGCN method on four public HSI datasets, demonstrating its superiority over other advanced classification methods in terms of three metrics, i.e., overall accuracy (OA), average accuracy (AA), and kappa coefficient (KC).

Growing studies reveal that Circular RNAs (circRNAs) are broadly engaged in physiological processes of cell proliferation, differentiation, aging, apoptosis, and are closely associated with the pathogenesis of numerous diseases. Clarification of the correlation among diseases and circRNAs is of great clinical importance to provide new therapeutic strategies for complex diseases. However, previous circRNA-disease association prediction methods rely excessively on the graph network, and the model performance is dramatically reduced when noisy connections occur in the graph structure. To address this problem, this paper proposes an unsupervised deep graph structure learning method GSLCDA to predict potential CDAs. Concretely, we first integrate circRNA and disease multi-source data to constitute the CDA heterogeneous network. Then the network topology is learned using the graph structure, and the original graph is enhanced in an unsupervised manner by maximize the inter information of the learned and original graphs to uncover their essential features. Finally, graph space sensitive k-nearest neighbor (KNN) algorithm is employed to search for latent CDAs. In the benchmark dataset, GSLCDA obtained 92.67% accuracy with 0.9279 AUC. GSLCDA also exhibits exceptional performance on independent datasets. Furthermore, 14, 12 and 14 of the top 16 circRNAs with the most points GSLCDA prediction scores were confirmed in the relevant literature in the breast cancer, colorectal cancer and lung cancer case studies, respectively. Such results demonstrated that GSLCDA can validly reveal underlying CDA and offer new perspectives for the diagnosis and therapy of complex human diseases.

With a growing body of evidence establishing circular RNAs (circRNAs) are widely exploited in eukaryotic cells and have a significant contribution in the occurrence and development of many complex human diseases. Disease-associated circRNAs can serve as clinical diagnostic biomarkers and therapeutic targets, providing novel ideas for biopharmaceutical research. However, available computation methods for predicting circRNA-disease associations (CDAs) do not sufficiently consider the contextual information of biological network nodes, making their performance limited. In this work, we propose a multi-hop attention graph neural network-based approach MAGCDA to infer potential CDAs. Specifically, we first construct a multi-source attribute heterogeneous network of circRNAs and diseases, then use a multi-hop strategy of graph nodes to deeply aggregate node context information through attention diffusion, thus enhancing topological structure information and mining data hidden features, and finally use random forest to accurately infer potential CDAs. In the four gold standard data sets, MAGCDA achieved prediction accuracy of 92.58%, 91.42%, 83.46% and 91.12%, respectively. MAGCDA has also presented prominent achievements in ablation experiments and in comparisons with other models. Additionally, 18 and 17 potential circRNAs in top 20 predicted scores for MAGCDA prediction scores were confirmed in case studies of the complex diseases breast cancer and Almozheimer's disease, respectively. These results suggest that MAGCDA can be a practical tool to explore potential disease-associated circRNAs and provide a theoretical basis for disease diagnosis and treatment.

MOTIVATION: Cell-type-specific gene expression is maintained in large part by transcription factors (TFs) selectively binding to distinct sets of sites in different cell types. Recent research works have provided evidence that such cell-type-specific binding is determined by TF's intrinsic sequence preferences, cooperative interactions with co-factors, cell-type-specific chromatin landscapes and 3D chromatin interactions. However, computational prediction and characterization of cell-type-specific and shared binding sites is rarely studied. RESULTS: In this article, we propose two computational approaches for predicting and characterizing cell-type-specific and shared binding sites by integrating multiple types of features, in which one is based on XGBoost and another is based on convolutional neural network (CNN). To validate the performance of our proposed approaches, ChIP-seq datasets of 10 binding factors were collected from the GM12878 (lymphoblastoid) and K562 (erythroleukemic) human hematopoietic cell lines, each of which was further categorized into cell-type-specific (GM12878- and K562-specific) and shared binding sites. Then, multiple types of features for these binding sites were integrated to train the XGBoost- and CNN-based models. Experimental results show that our proposed approaches significantly outperform other competing methods on three classification tasks. Moreover, we identified independent feature contributions for cell-type-specific and shared sites through SHAP values and explored the ability of the CNN-based model to predict cell-type-specific and shared binding sites by excluding or including DNase signals. Furthermore, we investigated the generalization ability of our proposed approaches to different binding factors in the same cellular environment. AVAILABILITY AND IMPLEMENTATION: The source code is available at: https://github.com/turningpoint1988/CSSBS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Accumulating evidence suggests that circRNAs play crucial roles in human diseases. CircRNA-disease association prediction is extremely helpful in understanding pathogenesis, diagnosis, and prevention, as well as identifying relevant biomarkers. During the past few years, a large number of deep learning (DL) based methods have been proposed for predicting circRNA-disease association and achieved impressive prediction performance. However, there are two main drawbacks to these methods. The first is these methods underutilize biometric information in the data. Second, the features extracted by these methods are not outstanding to represent association characteristics between circRNAs and diseases. In this study, we developed a novel deep learning model, named iCircDA-NEAE, to predict circRNA-disease associations. In particular, we use disease semantic similarity, Gaussian interaction profile kernel, circRNA expression profile similarity, and Jaccard similarity simultaneously for the first time, and extract hidden features based on accelerated attribute network embedding (AANE) and dynamic convolutional autoencoder (DCAE). Experimental results on the circR2Disease dataset show that iCircDA-NEAE outperforms other competing methods significantly. Besides, 16 of the top 20 circRNA-disease pairs with the highest prediction scores were validated by relevant literature. Furthermore, we observe that iCircDA-NEAE can effectively predict new potential circRNA-disease associations.

DNA-binding proteins (DBPs) play vital roles in the regulation of biological systems. Although there are already many deep learning methods for predicting the sequence specificities of DBPs, they face two challenges as follows. Classic deep learning methods for DBPs prediction usually fail to capture the dependencies between genomic sequences since their commonly used one-hot codes are mutually orthogonal. Besides, these methods usually perform poorly when samples are inadequate. To address these two challenges, we developed a novel language model for mining DBPs using human genomic data and ChIP-seq datasets with decaying learning rates, named DNA Fine-tuned Language Model (DFLM). It can capture the dependencies between genome sequences based on the context of human genomic data and then fine-tune the features of DBPs tasks using different ChIP-seq datasets. First, we compared DFLM with the existing widely used methods on 69 datasets and we achieved excellent performance. Moreover, we conducted comparative experiments on complex DBPs and small datasets. The results show that DFLM still achieved a significant improvement. Finally, through visualization analysis of one-hot encoding and DFLM, we found that one-hot encoding completely cut off the dependencies of DNA sequences themselves, while DFLM using language models can well represent the dependency of DNA sequences. Source code are available at: https://github.com/Deep-Bioinfo/DFLM.

Transcription factors (TFs) play a part in gene expression. TFs can form complex gene expression regulation system by combining with DNA. Thereby, identifying the binding regions has become an indispensable step for understanding the regulatory mechanism of gene expression. Due to the great achievements of applying deep learning (DL) to computer vision and language processing in recent years, many scholars are inspired to use these methods to predict TF binding sites (TFBSs), achieving extraordinary results. However, these methods mainly focus on whether DNA sequences include TFBSs. In this paper, we propose a fully convolutional network (FCN) coupled with refinement residual block (RRB) and global average pooling layer (GAPL), namely FCNARRB. Our model could classify binding sequences at nucleotide level by outputting dense label for input data. Experimental results on human ChIP-seq datasets show that the RRB and GAPL structures are very useful for improving model performance. Adding GAPL improves the performance by 9.32% and 7.61% in terms of IoU (Intersection of Union) and PRAUC (Area Under Curve of Precision and Recall), and adding RRB improves the performance by 7.40% and 4.64%, respectively. In addition, we find that conservation information can help locate TFBSs.

Clinical evidence began to accumulate, suggesting that circRNAs can be novel therapeutic targets for various diseases and play a critical role in human health. However, limited by the complex mechanism of circRNA, it is difficult to quickly and large-scale explore the relationship between disease and circRNA in the wet-lab experiment. In this work, we design a new computational model MGRCDA on account of the metagraph recommendation theory to predict the potential circRNA-disease associations. Specifically, we first regard the circRNA-disease association prediction problem as the system recommendation problem, and design a series of metagraphs according to the heterogeneous biological networks; then extract the semantic information of the disease and the Gaussian interaction profile kernel (GIPK) similarity of circRNA and disease as network attributes; finally, the iterative search of the metagraph recommendation algorithm is used to calculate the scores of the circRNA-disease pair. On the gold standard dataset circR2Disease, MGRCDA achieved a prediction accuracy of 92.49% with an area under the ROC curve of 0.9298, which is significantly higher than other state-of-the-art models. Furthermore, among the top 30 disease-related circRNAs recommended by the model, 25 have been verified by the latest published literature. The experimental results prove that MGRCDA is feasible and efficient, and it can recommend reliable candidates to further wet-lab experiment and reduce the scope of the experiment.

Traffic prediction is the core task of intelligent transportation system (ITS) and accurate traffic prediction can greatly improve the utilization of public resources. Dynamic interaction of multiple spatial relationships will influence the accuracy of traffic prediction. However, many existing methods only consider static spatial relationships, which restricts the accuracy of the prediction. To address the above problem, in this article, we propose the Dynamic Multi-Graph Fusion Network (DMGF-Net) to model the spatial-temporal correlations in traffic network. In the DMGF-Net, the fusion graph is designed to leverage and extract the various spatial correlations between different regions by fusing spatial graph, semantic graph, and spatial-semantic graph. Further, to dynamically learn the importance of different neighbors, we design the Dynamic Spatial-Temporal Unit (DSTU), which can adjust the aggregation weights of different neighbors by combining the convolution operation and the attention mechanism. It can selectively aggregate spatial-temporal features from different neighbors. Extensive experiments on three datasets demonstrate that effectiveness of our model, especially on PEMS08, our model achieves an increase of about 8.55% and 7.55% in terms of MAE and RMSE than the static model STGCN.

Drug-drug interaction (DDI) plays an important role in drug development and administration. Most of existing network-based computation models regard the DDI prediction as a binary classification problem and generate negative DDI samples randomly, but the binary classification is not in line with the real problem since there are dozens of types of DDI and randomly generating negative samples may introduce false-negative samples since the non-observed facts can be either false or just missing. To address the above limitations, we propose a new framework called KG2ECapsule that explicitly models the multi-relational DDI data based on biomedical knowledge graphs in an end-to-end fashion. It first generates high-quality negative samples based on the average number of tail entities and head entities for each relation to reduce false-negative samples to some extent. KG2ECapsule then refines the representations of entities by recursively propagating the embeddings from the attention-based receptive fields of entities. Empirical results on three biomedical knowledge graphs of different scales show that KG2ECapsule outperforms the state-of-the-art methods consistently in multi-label DDI prediction task and further studies verify the efficacy of both probability-based sampling strategy and non-linear transformation for modeling multi-relational data.

Graph is a natural data structure for describing complex systems, which contains a set of objects and relationships. Ubiquitous real-life biomedical problems can be modeled as graph analytics tasks. Machine learning, especially deep learning, succeeds in vast bioinformatics scenarios with data represented in Euclidean domain. However, rich relational information between biological elements is retained in the non-Euclidean biomedical graphs, which is not learning friendly to classic machine learning methods. Graph representation learning aims to embed graph into a low-dimensional space while preserving graph topology and node properties. It bridges biomedical graphs and modern machine learning methods and has recently raised widespread interest in both machine learning and bioinformatics communities. In this work, we summarize the advances of graph representation learning and its representative applications in bioinformatics. To provide a comprehensive and structured analysis and perspective, we first categorize and analyze both graph embedding methods (homogeneous graph embedding, heterogeneous graph embedding, attribute graph embedding) and graph neural networks. Furthermore, we summarize their representative applications from molecular level to genomics, pharmaceutical and healthcare systems level. Moreover, we provide open resource platforms and libraries for implementing these graph representation learning methods and discuss the challenges and opportunities of graph representation learning in bioinformatics. This work provides a comprehensive survey of emerging graph representation learning algorithms and their applications in bioinformatics. It is anticipated that it could bring valuable insights for researchers to contribute their knowledge to graph representation learning and future-oriented bioinformatics studies.

In recent years, major advances have been made in various chromosome conformation capture technologies to further satisfy the needs of researchers for high-quality, high-resolution contact interactions. Discriminating the loops from genome-wide contact interactions is crucial for dissecting three-dimensional(3D) genome structure and function. Here, we present a deep learning method to predict genome-wide chromatin loops, called DLoopCaller, by combining accessible chromatin landscapes and raw Hi-C contact maps. Some available orthogonal data ChIA-PET/HiChIP and Capture Hi-C were used to generate positive samples with a wider contact matrix which provides the possibility to find more potential genome-wide chromatin loops. The experimental results demonstrate that DLoopCaller effectively improves the accuracy of predicting genome-wide chromatin loops compared to the state-of-the-art method Peakachu. Moreover, compared to two of most popular loop callers, such as HiCCUPS and Fit-Hi-C, DLoopCaller identifies some unique interactions. We conclude that a combination of chromatin landscapes on the one-dimensional genome contributes to understanding the 3D genome organization, and the identified chromatin loops reveal cell-type specificity and transcription factor motif co-enrichment across different cell lines and species.

Transcription factors (TFs) play an important role in regulating gene expression, thus the identification of the sites bound by them has become a fundamental step for molecular and cellular biology. In this paper, we developed a deep learning framework leveraging existing fully convolutional neural networks (FCN) to predict TF-DNA binding signals at the base-resolution level (named as FCNsignal). The proposed FCNsignal can simultaneously achieve the following tasks: (i) modeling the base-resolution signals of binding regions; (ii) discriminating binding or non-binding regions; (iii) locating TF-DNA binding regions; (iv) predicting binding motifs. Besides, FCNsignal can also be used to predict opening regions across the whole genome. The experimental results on 53 TF ChIP-seq datasets and 6 chromatin accessibility ATAC-seq datasets show that our proposed framework outperforms some existing state-of-the-art methods. In addition, we explored to use the trained FCNsignal to locate all potential TF-DNA binding regions on a whole chromosome and predict DNA sequences of arbitrary length, and the results show that our framework can find most of the known binding regions and accept sequences of arbitrary length. Furthermore, we demonstrated the potential ability of our framework in discovering causal disease-associated single-nucleotide polymorphisms (SNPs) through a series of experiments.

The abuse of traditional antibiotics has led to an increase in the resistance of bacteria and viruses. Similar to the function of antibacterial peptides, bacteriocins are more common as a kind of peptides produced by bacteria that have bactericidal or bacterial effects. More importantly, the marine environment is one of the most abundant resources for extracting marine microbial bacteriocins (MMBs). Identifying bacteriocins from marine microorganisms is a common goal for the development of new drugs. Effective use of MMBs will greatly alleviate the current antibiotic abuse problem. In this work, deep learning is used to identify meaningful MMBs. We propose a random multi-scale convolutional neural network method. In the scale setting, we set a random model to update the scale value randomly. The scale selection method can reduce the contingency caused by artificial setting under certain conditions, thereby making the method more extensive. The results show that the classification performance of the proposed method is better than the state-of-the-art classification methods. In addition, some potential MMBs are predicted, and some different sequence analyses are performed on these candidates. It is worth mentioning that after sequence analysis, the HNH endonucleases of different marine bacteria are considered as potential bacteriocins.

The eight papers in this special section were presented at the Sixteenth International Conference on Intelligent Computing (ICIC) that was held in Bari, Italy, on October 2-5, 2020. ICIC was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, and computational biology, etc. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems and solutions related to the multifaceted aspects of intelligent computing.

Attention mechanism has the ability to find important information in the sequence. The regions of the RNA sequence that can bind to proteins are more important than those that cannot bind to proteins. Neither conventional methods nor deep learning-based methods, they are not good at learning this information. In this study, LSTM is used to extract the correlation features between different sites in RNA sequence. We also use attention mechanism to evaluate the importance of different sites in RNA sequence. We get the optimal combination of k-mer length, k-mer stride window, k-mer sentence length, k-mer sentence stride window, and optimization function through hyper-parm experiments. The results show that the performance of our method is better than other methods. We tested the effects of changes in k-mer vector length on model performance. We show model performance changes under various k-mer related parameter settings. Furthermore, we investigate the effect of attention mechanism and RNA structure data on model performance.

Discovery of transcription factor binding sites (TFBSs) is of primary importance for understanding the underlying binding mechanic and gene regulation process. Growing evidence indicates that apart from the primary DNA sequences, DNA shape landscape has a significant influence on transcription factor binding preference. To effectively model the co-influence of sequence and shape features, we emphasize the importance of position information of sequence motif and shape pattern. In this paper, we propose a novel deep learning-based architecture, named hybridShape eDeepCNN, for TFBS prediction which integrates DNA sequence and shape information in a spatially aligned manner. Our model utilizes the power of the multi-layer convolutional neural network and constructs an independent subnetwork to adapt for the distinct data distribution of heterogeneous features. Besides, we explore the usage of continuous embedding vectors as the representation of DNA sequences. Based on the experiments on 20 in-vitro datasets derived from universal protein binding microarrays (uPBMs), we demonstrate the superiority of our proposed method and validate the underlying design logic.

Dynamic pricing plays an important role in solving the problems such as traffic load reduction, congestion control, and revenue improvement. Efficient dynamic pricing strategies can increase capacity utilization, total revenue of service providers, and the satisfaction of both passengers and drivers. Many proposed dynamic pricing technologies focus on short-term optimization and face poor scalability in modeling long-term goals for the limitations of solution optimality and prohibitive computation. In this article, a deep reinforcement learning framework is proposed to tackle the dynamic pricing problem for ride-hailing platforms. A soft actor-critic (SAC) algorithm is adopted in the reinforcement learning framework. First, the dynamic pricing problem is translated into a Markov Decision Process (MDP) and is set up in continuous action spaces, which is no need for the discretization of action space. Then, a new reward function is obtained by the order response rate and the KL-divergence between supply distribution and demand distribution. Experiments and case studies demonstrate that the proposed method outperforms the baselines in terms of order response rate and total revenue.

Traffic flow prediction is the upstream problem of path planning, intelligent transportation system, and other tasks. Many studies have been carried out on the traffic flow prediction of the spatio-temporal network, but the effects of spatio-temporal flexibility (historical data of the same type of time intervals in the same location will change flexibly) and spatio-temporal correlation (different road conditions have different effects at different times) have not been considered at the same time. We propose the Deep Spatio-temporal Adaptive 3D Convolution Neural Network (ST-A3DNet), which is a new scheme to solve both spatio-temporal correlation and flexibility, and consider spatio-temporal complexity (complex external factors, such as weather and holidays). Different from other traffic forecasting models, ST-A3DNet captures the spatio-temporal relationship at the same time through the Adaptive 3D convolution module, assigns different weights flexibly according to the influence of historical data, and obtains the impact of external factors on the flow through the ex-mask module. Considering the holidays and weather conditions, we train our model for experiments in Xi’an and Chengdu. We evaluate the ST-A3DNet and the results show that we have better results than the other 11 baselines.

Increasing evidence suggest that circRNA, as one of the most promising emerging biomarkers, has a very close relationship with diseases. Exploring the relationship between circRNA and diseases can provide novel perspective for diseases diagnosis and pathogenesis. The existing circRNA-disease association (CDA) prediction models, however, generally treat the data attributes equally, do not pay special attention to the attributes with more significant influence, and do not make full use of the correlation and symbiosis between attributes to dig into the latent semantic information of the data. Therefore, in response to the above problems, this paper proposes a natural semantic enhancement method NSECDA to predict CDA. In practical terms, we first recognize the circRNA sequence as a biological language, and analyze its natural semantic properties through the natural language understanding theory; then integrate it with disease attributes, circRNA and disease Gaussian Interaction Profile (GIP) kernel attributes, and use Graph Attention Network (GAT) to focus on the influential attributes, so as to mine the deeply hidden features; finally, the Rotation Forest (RoF) classifier was used to accurately determine CDA. In the gold standard data set CircR2Disease, NSECDA achieved 92.49% accuracy with 0.9225 AUC score. In comparison with the non-natural semantic enhancement model and other classifier models, NSECDA also shows competitive performance. Additionally, 25 of the CDA pairs with unknown associations in the top 30 prediction scores of NSECDA have been proven by newly reported studies. These achievements suggest that NSECDA is an effective model to predict CDA, which can provide credible candidate for subsequent wet experiments, thus significantly reducing the scope of investigations.

Effectively representing Medical Subject Headings (MeSH) headings (terms) such as disease and drug as discriminative vectors could greatly improve the performance of downstream computational prediction models. However, these terms are often abstract and difficult to quantify. In this paper, we converted the MeSH tree structure into a relationship network and applied several graph embedding algorithms on it to represent these terms. Specifically, the relationship network consisting of nodes (MeSH headings) and edges (relationships), which can be constructed by the tree num. Then, five graph embedding algorithms including DeepWalk, LINE, SDNE, LAP and HOPE were implemented on the relationship network to represent MeSH headings as vectors. In order to evaluate the performance of the proposed methods, we carried out the node classification and relationship prediction tasks. The results show that the MeSH headings characterized by graph embedding algorithms can not only be treated as an independent carrier for representation, but also can be utilized as additional information to enhance the representation ability of vectors. Thus, it can serve as an input and continue to play a significant role in any computational models related to disease, drug, microbe, etc. Besides, our method holds great hope to inspire relevant researchers to study the representation of terms in this network perspective.

DNA/RNA motif mining is the foundation of gene function research. The DNA/RNA motif mining plays an extremely important role in identifying the DNA- or RNA-protein binding site, which helps to understand the mechanism of gene regulation and management. For the past few decades, researchers have been working on designing new efficient and accurate algorithms for mining motif. These algorithms can be roughly divided into two categories: the enumeration approach and the probabilistic method. In recent years, machine learning methods had made great progress, especially the algorithm represented by deep learning had achieved good performance. Existing deep learning methods in motif mining can be roughly divided into three types of models: convolutional neural network (CNN) based models, recurrent neural network (RNN) based models, and hybrid CNN-RNN based models. We introduce the application of deep learning in the field of motif mining in terms of data preprocessing, features of existing deep learning architectures and comparing the differences between the basic deep learning models. Through the analysis and comparison of existing deep learning methods, we found that the more complex models tend to perform better than simple ones when data are sufficient, and the current methods are relatively simple compared with other fields such as computer vision, language processing (NLP), computer games, etc. Therefore, it is necessary to conduct a summary in motif mining by deep learning, which can help researchers understand this field.

Transcription factors (TFs) play an important role in regulating gene expression, thus identification of the regions bound by them has become a fundamental step for molecular and cellular biology. In recent years, an increasing number of deep learning (DL) based methods have been proposed for predicting TF binding sites (TFBSs) and achieved impressive prediction performance. However, these methods mainly focus on predicting the sequence specificity of TF-DNA binding, which is equivalent to a sequence-level binary classification task, and fail to identify motifs and TFBSs accurately. In this paper, we developed a fully convolutional network coupled with global average pooling (FCNA), which by contrast is equivalent to a nucleotide-level binary classification task, to roughly locate TFBSs and accurately identify motifs. Experimental results on human ChIP-seq datasets show that FCNA outperforms other competing methods significantly. Besides, we find that the regions located by FCNA can be used by motif discovery tools to further refine the prediction performance. Furthermore, we observe that FCNA can accurately identify TF-DNA binding motifs across different cell lines and infer indirect TF-DNA bindings.

A key aim of post-genomic biomedical research is to systematically understand molecules and their interactions in human cells. Multiple biomolecules coordinate to sustain life activities, and interactions between various biomolecules are interconnected. However, existing studies usually only focusing on associations between two or very limited types of molecules. In this study, we propose a network representation learning based computational framework MAN-SDNE to predict any intermolecular associations. More specifically, we constructed a large-scale molecular association network of multiple biomolecules in human by integrating associations among long non-coding RNA, microRNA, protein, drug, and disease, containing 6,528 molecular nodes, 9 kind of,105,546 associations. And then, the feature of each node is represented by its network proximity and attribute features. Furthermore, these features are used to train Random Forest classifier to predict intermolecular associations. MAN-SDNE achieves a remarkable performance with an AUC of 0.9552 and an AUPR of 0.9338 under five-fold cross-validation. To indicate the ability to predict specific types of interactions, a case study for predicting lncRNA-protein interactions using MAN-SDNE is also executed. Experimental results demonstrate this work offers a systematic insight for understanding the synergistic associations between molecules and complex diseases and provides a network-based computational tool to systematically explore intermolecular interactions.

Discovery of transcription factor binding sites (TFBSs) is essential for understanding the underlying binding mechanisms and cellular functions. Recently, Convolutional neural network (CNN) has succeeded in predicting TFBSs from the primary DNA sequences. In addition to DNA sequences, several evidences suggest that protein-DNA binding is partly mediated by properties of DNA shape. Although many methods have been proposed to jointly account for DNA sequences and shape properties in predicting TFBSs, they ignore the power of the combination of deep learning and DNA sequence + shape. Therefore we develop a deep-learning-based sequence + shape framework (DLBSS) in this paper, which appropriately integrates DNA sequences and shape properties, to better understand protein-DNA binding preference. This method uses a shared CNN to find their common patterns from DNA sequences and their corresponding shape features, which are then concatenated to compute a predicted value. Using 66 in-vitro datasets derived from universal protein binding microarrays (uPBMs), we show that our proposed method DLBSS significantly improves the performance of predicting TFBSs. In addition, we explain the reason why we should use the shared CNN, and explore the performance of DLBSS when using a deeper CNN, through a series of experiments.

The rapid development of high-throughput sequencing technology provides unique opportunities for studying of transcription factor binding sites, but also brings new computational challenges. Recently, a series of discriminative motif discovery (DMD) methods have been proposed and offer promising solutions for addressing these challenges. However, because of the huge computation cost, most of them have to choose approximate schemes that either sacrifice the accuracy of motif representation or tune motif parameter indirectly. In this paper, we propose a bag-based classifier combined with a multi-fold learning scheme (BCMF) to discover motifs from ChIP-seq datasets. First, BCMF formulates input sequences as a labeled bag naturally. Then, a bag-based classifier, combining with a bag feature extracting strategy, is applied to construct the objective function, and a multi-fold learning scheme is used to solve it. Compared with the existing DMD tools, BCMF features three improvements: 1) Learning position weight matrix (PWM) directly in a continuous space; 2) Proposing to represent a positive bag with a feature fused by its k "most positive" patterns. 3) Applying a more advanced learning scheme. The experimental results on 134 ChIP-seq datasets show that BCMF substantially outperforms existing DMD methods (including DREME, HOMER, XXmotif, motifRG, EDCOD and our previous work).

Discovering DNA-protein binding sites, also known as motif discovery, is the foundation for further analysis of transcription factors (TFs). Deep learning algorithms such as convolutional neural networks (CNN) have been introduced to motif discovery task and have achieved state-of-art performance. However, due to the limitations of CNN, motif discovery methods based on CNN do not take full advantage of large-scale sequencing data generated by high-throughput sequencing technology. Hence, in this paper we propose multi-scale capsule network architecture (MSC) integrating multi-scale CNN, a variant of CNN able to extract motif features of different lengths, and capsule network, a novel type of artificial neural network architecture aimed at improving CNN. The proposed method is tested on real ChIP-seq datasets and the experimental results show a considerable improvement compared with two well-tested deep learning-based sequence model, DeepBind and Deepsea.

Facial landmark detection is a fundamental research topic in computer vision that is widely adopted in many applications. Recently, thanks to the development of convolutional neural networks, this topic has been largely improved. This article proposes facial-landmark detector, which is based on a state-of-the-art architecture for landmark localization called stacked hourglass network, to obtain accurate facial landmark-points. More specifically, this article uses residual networks as the backbone instead of a 7 × 7 convolution layer. Additionally, it modifies the hourglass modules by using the residual-dense blocks in the mainstream for capturing more efficient features and the 1 × 1 convolution layers in the branch streams for reducing the model size and computational time, instead of the original residual blocks. The proposed architecture also enhances the features from modified hourglass modules with finer-resolution features via a lateral connection to generate more accurate results. The proposed network can outperform other state-of-the-art methods on the AFLW2000-3D dataset and the LS3D-W dataset, the largest three-dimensional (3-D face) alignment dataset to date.

MOTIVATION: Emerging evidence indicates that circular RNA (circRNA) plays a crucial role in human disease. Using circRNA as biomarker gives rise to a new perspective regarding our diagnosing of diseases and understanding of disease pathogenesis. However, detection of circRNA-disease associations by biological experiments alone is often blind, limited to small scale, high cost and time consuming. Therefore, there is an urgent need for reliable computational methods to rapidly infer the potential circRNA-disease associations on a large scale and to provide the most promising candidates for biological experiments. RESULTS: In this article, we propose an efficient computational method based on multi-source information combined with deep convolutional neural network (CNN) to predict circRNA-disease associations. The method first fuses multi-source information including disease semantic similarity, disease Gaussian interaction profile kernel similarity and circRNA Gaussian interaction profile kernel similarity, and then extracts its hidden deep feature through the CNN and finally sends them to the extreme learning machine classifier for prediction. The 5-fold cross-validation results show that the proposed method achieves 87.21% prediction accuracy with 88.50% sensitivity at the area under the curve of 86.67% on the CIRCR2Disease dataset. In comparison with the state-of-the-art SVM classifier and other feature extraction methods on the same dataset, the proposed model achieves the best results. In addition, we also obtained experimental support for prediction results by searching published literature. As a result, 7 of the top 15 circRNA-disease pairs with the highest scores were confirmed by literature. These results demonstrate that the proposed model is a suitable method for predicting circRNA-disease associations and can provide reliable candidates for biological experiments. AVAILABILITY AND IMPLEMENTATION: The source code and datasets explored in this work are available at https://github.com/look0012/circRNA-Disease-association. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The papers in this special section were presented at the Fourteenth International Conference on Intelligent Computing (ICIC) held in Wuhan, China, on August 15-18, 2018.

Breast cancer is one of the most common cancers all over the world, which bring about more than 450,000 deaths each year. Although this malignancy has been extensively studied by a large number of researchers, its prognosis is still poor. Since therapeutic advance can be obtained based on gene signatures, there is an urgent need to discover genes related to breast cancer that may help uncover the mechanisms in cancer progression. We propose a deep learning method for the discovery of breast cancer-related genes by using Capsule Network based Modeling of Multi-omics Data (CapsNetMMD). In CapsNetMMD, we make use of known breast cancer-related genes to transform the issue of gene identification into the issue of supervised classification. The features of genes are generated through comprehensive integration of multi-omics data, e.g., mRNA expression, z scores for mRNA expression, DNA methylation, and two forms of DNA copy-number alterations (CNAs). By modeling features based on the capsule network, we identify breast cancer-related genes with a significantly better performance than other existing machine learning methods. The predicted genes with prognostic values play potential important roles in breast cancer and may serve as candidates for biologists and medical scientists in the future studies of biomarkers.

RNA-Protein binding is involved in many different biological processes. With the progress of technology, more and more data are available for research. Based on these data, many prediction methods have been proposed to predict RNA-Protein binding preference. Some of these methods use only RNA sequence features for prediction, and some methods use multiple features for prediction. But, the performance of these methods is not satisfactory. In this study, we propose an improved capsule network to predict RNA-protein binding preferences, which can use both RNA sequence features and structure features. Experimental results show that our proposed method iCapsule performs better than three baseline methods in this field. We used both RNA sequence features and structure features in the model, so we tested the effect of primary capsule layer changes on model performance. In addition, we also studied the impact of model structure on model performance by performing our proposed method with different number of convolution layers and different kernel sizes.

RNA-Protein binding plays important roles in the field of gene expression. With the development of high throughput sequencing, several conventional methods and deep learning-based methods have been proposed to predict the binding preference of RNA-protein binding. These methods can hardly meet the need of consideration of the dependencies between subsequence and the various motif lengths of different translation factors (TFs). To overcome such limitations, we propose a predictive model that utilizes a combination of multi-scale convolutional layers and bidirectional gated recurrent unit (GRU) layer. Multi-scale convolution layer has the ability to capture the motif features of different lengths, and bidirectional GRU layer is able to capture the dependencies among subsequence. Experimental results show that the proposed method performs better than four state-of-the-art methods in this field. In addition, we investigate the effect of model structure on model performance by performing our proposed method with a different convolution layer and a different number of kernel size. We also demonstrate the effectiveness of bidirectional GRU in improving model performance through comparative experiments.

Emerging evidence has shown that RNA plays a crucial role in many cellular processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological experiments provide a lot of valuable information for the initial identification of RNA-protein interactions (RPIs), but with the increasing complexity of RPIs networks, this method gradually falls into expensive and time-consuming situations. Therefore, there is an urgent need for high speed and reliable methods to predict RNA-protein interactions. In this study, we propose a computational method for predicting the RNA-protein interactions using sequence information. The deep learning convolution neural network (CNN) algorithm is utilized to mine the hidden high-level discriminative features from the RNA and protein sequences and feed it into the extreme learning machine (ELM) classifier. The experimental results with 5-fold cross-validation indicate that the proposed method achieves superior performance on benchmark datasets (RPI1807, RPI2241, and RPI369) with the accuracy of 98.83, 90.83, and 85.63 percent, respectively. We further evaluate the performance of the proposed model by comparing it with the state-of-the-art SVM classifier and other existing methods on the same benchmark data set. In addition, we predicted the independent NPInter v2.0 data set using the model trained on RPI369. The experimental results show that our model can serve as a useful tool for predicting RNA-protein interactions.

Although convolutional neural networks (CNN) have outperformed conventional methods in predicting the sequence specificities of protein-DNA binding in recent years, they do not take full advantage of the intrinsic weakly-supervised information of DNA sequences that a bound sequence may contain multiple TFBS(s). Here, we propose a weakly-supervised convolutional neural network architecture (WSCNN), combining multiple-instance learning (MIL) with CNN, to further boost the performance of predicting protein-DNA binding. WSCNN first divides each DNA sequence into multiple overlapping subsequences (instances) with a sliding window, and then separately models each instance using CNN, and finally fuses the predicted scores of all instances in the same bag using four fusion methods, including Max, Average, Linear Regression, and Top-Bottom Instances. The experimental results on in vivo and in vitro datasets illustrate the performance of the proposed approach. Moreover, models built on in vitro data using WSCNN can predict in vivo protein-DNA binding with good accuracy. In addition, we give a quantitative analysis of the importance of the reverse-complement mode in predicting in vivo protein-DNA binding, and explain why not directly use advanced pooling layers to combine MIL with CNN, through a series of experiments.

The spatial independence and temporal continuity of video data as a whole are not fully investigated for video action detection. To tackle this issue, a deep network architecture is proposed, named Pseudo-3D Convolutional Tube Network (P3D-CTN). In particular, the proposed P3D-CTN integrates the frame-based two-dimensional convolutional module with the P3D convolutional module to balance the spatial and temporal information, and generates deeper features about human actions. Evaluations on two benchmark datasets (i.e., UCF-Sports and J-HMDB) demonstrate that the proposed P3D-CTN has superior performances in the task of action label prediction and yields state-of-the-art results for spatio-temporal action detection.

Deep Convolutional Neural Network has shown significant improvements in many fields of computer vision, and a series of researches are proposed to explore advanced model structures to attenuate the problem of over-fitting. In this paper, two data driven techniques are designed including SwitchNode and SwitchConnect, which employ the sparsity of deterministic data to regularize convolutional neural network models. Specifically, the proposed SwitchNode method switches from the redundant nodes which have similar activations and spatial information to new initialization nodes, while the SwitchConnect method retrains replaceable convolutional kernels. The effectiveness of the proposed data driven regularization methods has been verified by the performance gain experimented on several benchmark image classification datasets.

The papers presented in this special section were presented at the Thirteenth International Conference on Intelligent Computing (ICIC) that was held in Liverpool, UK, on August 7-10, 2017. ICIC was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, and computational biology, etc. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems, and solutions related to the multifaceted aspects of intelligent computing.

Underlying a cancer phenotype is a specific gene regulatory network that represents the complex regulatory relationships between genes. It remains, however, a challenge to find cancer-related gene regulatory network because of insufficient sample sizes and complex regulatory mechanisms in which gene is influenced by not only other genes but also other biological factors. With the development of high-throughput technologies and the unprecedented wealth of multi-omics data it gives us a new opportunity to design machine learning method to investigate underlying gene regulatory network. In this paper, we propose an approach, which use Biweight Midcorrelation to measure the correlation between factors and make use of Nonconvex Penalty based sparse regression for Gene Regulatory Network inference (BMNPGRN). BMNCGRN incorporates multi-omics data (including DNA methylation and copy number variation) and their interactions in gene regulatory network model. The experimental results on synthetic datasets show that BMNPGRN outperforms popular and state-of-the-art methods (including DCGRN, ARACNE, and CLR) under false positive control. Furthermore, we applied BMNPGRN on breast cancer (BRCA) data from The Cancer Genome Atlas database and provided gene regulatory network.

Although Deep learning algorithms have outperformed conventional methods in predicting the sequence specificities of DNA-protein binding, they lack to consider the dependencies among nucleotides and the diverse binding lengths for different transcription factors (TFs). To address the above two limitations simultaneously, in this paper, we propose a high-order convolutional neural network architecture (HOCNN), which employs a high-order encoding method to build high-order dependencies among nucleotides, and a multi-scale convolutional layer to capture the motif features of different length. The experimental results on real ChIP-seq datasets show that the proposed method outperforms the state-of-the-art deep learning method (DeepBind) in the motif discovery task. In addition, we provide further insights about the importance of introducing additional convolutional kernels and the degeneration problem of importing high-order in the motif discovery task.

Object co-segmentation aims at simultaneously extracting common objects appeared in multiple images. In this paper, we propose a novel object co-segmentation method in which we formulate the image co-segmentation as a locally biased discriminative clustering problem. Specifically, we add a seed vector and a constraint term into the framework of discriminative clustering to constrain the segmentation result bias to this seed vector. In order to deal with the co-segmentation problem with indefinite number of common foreground objects, we design a Markov Random Field (MRF) based method to extract common objects prior. The extracted common objects prior is then added into the discriminative clustering and treated as the seed vector to constrain the segmentation result biased to it. Under the supervision of this prior, the segmentation result can find more common objects and becomes more complete and meaningful. In addition, we present a new segmentation process where we alternately update the MRF-based model and discriminative clustering to refine the common objects prior and the segmentation result. Finally, we test the proposed method on three benchmark datasets, iCoseg, Coseg-Rep, and THUR15K. The experimental results demonstrate that the proposed method outperforms other state-of-the-art methods.

Type 2 Diabetes condition is a multifactorial disorder involves the convergence of genetics, environment, diet and lifestyle risk factors. This paper investigates genetic and conventional (clinical, sociodemographic) risk factors and their predictive power in classifying Type 2 Diabetes. Six statistically significant Single Nucleotide Polymorphisms (SNPs) associated with Type 2 Diabetes are derived by conducting logistic association analysis. The derived SNPs in addition to conventional risk factors are used to model supervised machine learning algorithms to classify cases and controls in genome wide association studies (GWAS). Models are trained using genetic variable analysis, genetic and conventional variable analysis, and conventional variable analysis. The results demonstrate of the three models, higher predictive capacity is evident when genetic and conventional predictors are combined. Using a Random Forest classifier, the Area Under the Curve=73.96%, Sensitivity=68.42 %, and Specificity=78.67%.

Motivation: Identification of enhancers and their strength is important because they play a critical role in controlling gene expression. Although some bioinformatics tools were developed, they are limited in discriminating enhancers from non-enhancers only. Recently, a two-layer predictor called 'iEnhancer-2L' was developed that can be used to predict the enhancer's strength as well. However, its prediction quality needs further improvement to enhance the practical application value. Results: A new predictor called 'iEnhancer-EL' was proposed that contains two layer predictors: the first one (for identifying enhancers) is formed by fusing an array of six key individual classifiers, and the second one (for their strength) formed by fusing an array of ten key individual classifiers. All these key classifiers were selected from 171 elementary classifiers formed by SVM (Support Vector Machine) based on kmer, subsequence profile and PseKNC (Pseudo K-tuple Nucleotide Composition), respectively. Rigorous cross-validations have indicated that the proposed predictor is remarkably superior to the existing state-of-the-art one in this area. Availability and implementation: A web server for the iEnhancer-EL has been established at http://bioinformatics.hitsz.edu.cn/iEnhancer-EL/, by which users can easily get their desired results without the need to go through the mathematical details. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: DNA replication is the key of the genetic information transmission, and it is initiated from the replication origins. Identifying the replication origins is crucial for understanding the mechanism of DNA replication. Although several discriminative computational predictors were proposed to identify DNA replication origins of yeast species, they could only be used to identify very tiny parts (250 or 300 bp) of the replication origins. Besides, none of the existing predictors could successfully capture the 'GC asymmetry bias' of yeast species reported by experimental observations. Hence it would not be surprising why their power is so limited. To grasp the CG asymmetry feature and make the prediction able to cover the entire replication regions of yeast species, we develop a new predictor called 'iRO-3wPseKNC'. Results: Rigorous cross validations on the benchmark datasets from four yeast species (Saccharomyces cerevisiae, Schizosaccharomyces pombe, Kluyveromyces lactis and Pichia pastoris) have indicated that the proposed predictor is really very powerful for predicting the entire DNA duplication origins. Availability and implementation: The web-server for the iRO-3wPseKNC predictor is available at http://bioinformatics.hitsz.edu.cn/iRO-3wPseKNC/, by which users can easily get their desired results without the need to go through the mathematical details. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Being responsible for initiating transaction of a particular gene in genome, promoter is a short region of DNA. Promoters have various types with different functions. Owing to their importance in biological process, it is highly desired to develop computational tools for timely identifying promoters and their types. Such a challenge has become particularly critical and urgent in facing the avalanche of DNA sequences discovered in the postgenomic age. Although some prediction methods were developed, they can only be used to discriminate a specific type of promoters from non-promoters. None of them has the ability to identify the types of promoters. This is due to the facts that different types of promoters may share quite similar consensus sequence pattern, and that the promoters of same type may have considerably different consensus sequences. Results: To overcome such difficulty, using the multi-window-based PseKNC (pseudo K-tuple nucleotide composition) approach to incorporate the short-, middle-, and long-range sequence information, we have developed a two-layer seamless predictor named as 'iPromoter-2 L'. The first layer serves to identify a query DNA sequence as a promoter or non-promoter, and the second layer to predict which of the following six types the identified promoter belongs to: σ24, σ28, σ32, σ38, σ54 and σ70. Availability and implementation: For the convenience of most experimental scientists, a user-friendly and publicly accessible web-server for the powerful new predictor has been established at http://bioinformatics.hitsz.edu.cn/iPromoter-2L/. It is anticipated that iPromoter-2 L will become a very useful high throughput tool for genome analysis. Contact: [email protected] or [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

In this study, the authors propose a new method to fuse multiple segmentations generated by different methods or same methods with different parameters. The proposed method has several contributions. First, they convert the image segmentation fusion problem into a weakly supervised learning problem. Thus, the information of superpixels can be used to guide the fusion process. Second, they treat the multiple segmentations as multiple closely related tasks and utilise multi‐task learning method to evaluate the reliability of the segmentations. Third, they design a strategy to ensemble the evaluated segmentation maps to obtain the final segmentation. The experiment on the benchmark dataset MSRC demonstrates the superior performance of the proposed method on image foreground and background segmentations.

Post translational modification plays a significiant role in the biological processing. The potential post translational modification is composed of the center sites and the adjacent amino acid residues which are fundamental protein sequence residues. It can be helpful to perform their biological functions and contribute to understanding the molecular mechanisms that are the foundations of protein design and drug design. The existing algorithms of predicting modified sites often have some shortcomings, such as lower stability and accuracy. In this paper, a combination of physical, chemical, statistical, and biological properties of a protein have been ulitized as the features, and a novel framework is proposed to predict a protein's post translational modification sites. The multi-layer neural network and support vector machine are invoked to predict the potential modified sites with the selected features that include the compositions of amino acid residues, the E-H description of protein segments, and several properties from the AAIndex database. Being aware of the possible redundant information, the feature selection is proposed in the propocessing step in this research. The experimental results show that the proposed method has the ability to improve the accuracy in this classification issue.

The eight papers included in this special section were presented at the 12th International Conference on Intelligent Computing (ICIC) held at Lanzhou, China, during August 2-5, 2016. ICIC was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, and computational biology, etc. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems, and solutions related to the multifaceted aspects of intelligent computing.

Gene set enrichment (GSE) is a useful tool for analyzing and interpreting large molecular datasets generated by modern biomedical science. The accuracy and reproducibility of GSE analysis are heavily affected by the quality and integrity of gene sets annotations. In this paper, we propose a novel method, robust trace-norm multitask learning, to solve the optimization problem of gene set annotations. Inspired by the binary nature of annotations, we convert the optimization of gene set annotations into a weakly supervised classification problem and use discriminative logistic regression to fit these datasets. Then, the output of logistic regression can be used to measure the probability of the existence of annotations. In addition, the optimization of each row of the annotation matrix can be treated as an independent weakly classification task, and we use the multitask learning approach with trace-norm regularization to optimize all rows of annotation matrix simultaneously. Finally, the experiments on simulated and real data demonstrate the effectiveness and good performance of the proposed method.

The transcription factors (TFs) can activate or suppress gene expression by binding to specific sites, hence are crucial regulatory elements for transcription. Recently, series of discriminative motif finders have been tailored to offering promising strategy for harnessing the power of large quantities of accumulated high-throughput experimental data. However, in order to achieve high speed, these algorithms have to sacrifice accuracy by employing simplified statistical models during the searching process. In this paper, we propose a novel approach named Discriminative Motif Learning via AUC (DiscMLA) to discover motifs on high-throughput datasets. Unlike previous approaches, DiscMLA tries to optimize with a more comprehensive criterion (AUC) during motifs searching. In addition, based on an experimental observation of motif identification on large-scale datasets, some novel procedures are designed to accelerate DiscMLA. The experimental results on 52 real-world datasets demonstrate that our approach substantially outperforms previous methods on discriminative motif learning problems. DiscMLA' stability, discriminability, and validity will help to exploit high-throughput datasets and answer many fundamental biological questions.

Although discriminative motif discovery (DMD) methods are promising for eliciting motifs from high-throughput experimental data, they usually have to sacrifice accuracy and may fail to fully leverage the potential of large datasets. Recently, it has been demonstrated that the motifs identified by DMDs can be significantly improved by maximizing the receiver-operating characteristic curve (AUC) metric, which has been widely used in the literature to rank the performance of elicited motifs. However, existing approaches for motif refinement choose to directly maximize the non-convex and discontinuous AUC itself, which is known to be difficult and may lead to suboptimal solutions. In this paper, we propose Large Margin Motif Optimizer (LMMO), a large-margin-type algorithm for refining regulatory motifs. By relaxing the AUC cost function with the surrogate convex hinge loss, we show that the resultant learning problem can be cast as an instance of difference-of-convex (DC) programs, and solve it iteratively using constrained concave-convex procedure (CCCP). To further save computational time, we combine LMMO with existing techniques for improving the scalability of large-margin-type algorithms, such as cutting plane method. Experimental evaluations on synthetic and real data illustrate the performance of the proposed approach. The code of LMMO is freely available at: https://github.com/ekffar/LMMO.

The rapid development of high-throughput sequencing technology provides unique opportunities for studies of transcription factor binding, while also bringing new computational challenges. Recently, a series of discriminative motif discovery (DMD) methods have been proposed and offer promising solutions for addressing these challenges. However, because of the huge computational cost, most of them have to choose approximate schemes that either sacrifice the accuracy of motif representation or tune motif parameter indirectly. In this paper, we propose Soft-bag based Motif Discovery (SMD) to discover motifs from ChIP-seq datasets. SMD formulates input sequences as a labeled bag naturally. Then, a generalized soft-margin SVM is applied to construct the objective function and a DMD-like scheme is designed to solve it. In contrast to those approximate learning strategies, SMD can optimize the motif PWM directly in a continuous space and be closer in accord with the known TF-DNA binding facts. The experimental results on real ChIP-seq datasets show that SMD substantially outperforms previous DMD methods (including DREME, HOMER and XXmotif).

The problem of object localization in image appear ubiquitously in computer vision applications including image classification, object detection and visual tracking. Recently, it is shown that multiple-instance learning (MIL) which is regarded as the fourth machine learning framework compared with supervised learning, unsupervised learning and reinforce learning has been verified that will get good effect in object localization in images. In this paper, we propose a novel method to solve the classical MIL problem, named Cross-Validated Smooth Multi-Instance learning (CVS-MIL). We treat the positiveness of instance as a continuous variable. The softmax model is used to bring a bridge between instances and bags and jointly optimize the bag label and instance label in a unified framework. The extensive experiments demonstrate that CVS-MIL consistently achieves superior performance on various MIL benchmarks. Moreover, we simply applied CVS-MIL to a challenging vision task, common object discovery. The state-of-the-art results of object discovery on Pascal VOC datasets further confirm the advantages of the proposed method.

The problem of matrix approximation appears ubiquitously in recommendation systems, computer vision and text mining. The prevailing assumption is that the partially observed matrix has a low-rank or can be well approximated by a low-rank matrix. However, this assumption is strictly that the partially observed matrix is globally low rank. In this paper, we propose a local sensitive formulation of matrix approximation which relaxes the global low-rank assumption, leading to a representation of the observed matrix as a weighted sum of low-rank matrices. We solve the problem by an efficient way based on the alternating direction method of multipliers (ADMM). Our experiments show improvements in prediction accuracy over classical approaches for recommendation tasks.

MOTIVATION: The discovery of transcription factor binding site (TFBS) motifs is essential for untangling the complex mechanism of genetic variation under different developmental and environmental conditions. Among the huge amount of computational approaches for de novo identification of TFBS motifs, discriminative motif learning (DML) methods have been proven to be promising for harnessing the discovery power of accumulated huge amount of high-throughput binding data. However, they have to sacrifice accuracy for speed and could fail to fully utilize the information of the input sequences. RESULTS: We propose a novel algorithm called CDAUC for optimizing DML-learned motifs based on the area under the receiver-operating characteristic curve (AUC) criterion, which has been widely used in the literature to evaluate the significance of extracted motifs. We show that when the considered AUC loss function is optimized in a coordinate-wise manner, the cost function of each resultant sub-problem is a piece-wise constant function, whose optimal value can be found exactly and efficiently. Further, a key step of each iteration of CDAUC can be efficiently solved as a computational geometry problem. Experimental results on real world high-throughput datasets illustrate that CDAUC outperforms competing methods for refining DML motifs, while being one order of magnitude faster. Meanwhile, preliminary results also show that CDAUC may also be useful for improving the interpretability of convolutional kernels generated by the emerging deep learning approaches for predicting TF sequences specificities. AVAILABILITY AND IMPLEMENTATION: CDAUC is available at: https://drive.google.com/drive/folders/0BxOW5MtIZbJjNFpCeHlBVWJHeW8 . CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Accumulating biological and clinical reports have indicated that imbalance of microbial community is closely associated with occurrence and development of various complex human diseases. Identifying potential microbe-disease associations, which could provide better understanding of disease pathology and further boost disease diagnostic and prognostic, has attracted more and more attention. However, hardly any computational models have been developed for large scale microbe-disease association prediction. RESULTS: In this article, based on the assumption that microbes with similar functions tend to share similar association or non-association patterns with similar diseases and vice versa, we proposed the model of Network Consistency Projection for Human Microbe-Disease Association prediction (NCPHMDA) by integrating known microbe-disease associations and Gaussian interaction profile kernel similarity for microbes and diseases. NCPHMDA yielded outstanding AUCs of 0.9039, 0.7953 and average AUC of 0.8918 in global leave-one-out cross validation, local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, colon cancer, asthma and type 2 diabetes were taken as independent case studies, where 9, 9 and 8 out of the top 10 predicted microbes were successfully confirmed by recent published clinical literature. CONCLUSION: NCPHMDA is a non-parametric universal network-based method which can simultaneously predict associated microbes for investigated diseases but does not require negative samples. It is anticipated that NCPHMDA would become an effective biological resource for clinical experimental guidance.

In this study, in order to take advantage of complementary information from different types of data for better disease status diagnosis, we combined gene expression with DNA methylation data and generated a fused network, based on which the stages of Kidney Renal Cell Carcinoma (KIRC) can be better identified. It is well recognized that a network is important for investigating the connectivity of disease groups. We exploited the potential of the network's features to identify the KIRC stage. We first constructed a patient network from each type of data. We then built a fused network based on network fusion method. Based on the link weights of patients, we used a generalized linear model to predict the group of KIRC subjects. Finally, the group prediction method was applied to test the power of network-based features. The performance (e.g., the accuracy of identifying cancer stages) when using the fused network from two types of data is shown to be superior to that when using two patient networks from only one data type. The work provides a good example for using network based features from multiple data types for a more comprehensive diagnosis.

Traditional gene selection methods for microarray data mainly considered the features' relevance by evaluating their utility for achieving accurate predication or exploiting data variance and distribution, and the selected genes were usually poorly explicable. To improve the interpretability of the selected genes as well as prediction accuracy, an improved gene selection method based on binary particle swarm optimization (BPSO) and prior information is proposed in this paper. In the proposed method, BPSO encoding gene-to-class sensitivity (GCS) information is used to perform gene selection. The gene-to-class sensitivity information, extracted from the samples by extreme learning machine (ELM), is encoded into the selection process in four aspects: initializing particles, updating the particles, modifying maximum velocity, and adopting mutation operation adaptively. Constrained by the gene-to-class sensitivity information, the new method can select functional gene subsets which are significantly sensitive to the samples' classes. With the few discriminative genes selected by the proposed method, ELM, K-nearest neighbor and support vector machine classifiers achieve much high prediction accuracy on five public microarray data, which in turn verifies the efficiency and effectiveness of the proposed gene selection method.

Disease comorbidity is the presence of one or more diseases along with a primary disorder, which causes additional pain to patients and leads to the failure of standard treatments compared with single diseases. Therefore, the identification of potential comorbidity can help prevent those comorbid diseases when treating a primary disease. Unfortunately, most of current known disease comorbidities are discovered occasionally in clinic, and our knowledge about comorbidity is far from complete. Despite the fact that many efforts have been made to predict disease comorbidity, the prediction accuracy of existing computational approaches needs to be improved. By investigating the factors underlying disease comorbidity, e.g., mutated genes and rewired protein-protein interactions (PPIs), we here present a novel algorithm to predict disease comorbidity by integrating multi-scale data ranging from genes to phenotypes. Benchmark results on real data show that our approach outperforms existing algorithms, and some of our novel predictions are validated with those reported in literature, indicating the effectiveness and predictive power of our approach. In addition, we identify some pathway and PPI patterns that underlie the co-occurrence between a primary disease and certain disease classes, which can help explain how the comorbidity is initiated from molecular perspectives.

The papers in this special section were presented at the 11th International Conference on Intelligent Computing (ICIC) held in Fuzhou, China, on August 20-23, 2015. This conference was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, etc. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems, and solutions related to the multifaceted aspects of intelligent computing.

This paper addresses the problem of accounting for confounding factors and expression quantitative trait loci (eQTL) mapping in the study of SNP-gene associations. The existing convex penalty based algorithm has limited capacity to keep main information of matrix in the process of reducing matrix rank. We present an algorithm, which use nonconvex penalty based low-rank representation to account for confounding factors and make use of sparse regression for eQTL mapping (NCLRS). The efficiency of the presented algorithm is evaluated by comparing the results of 18 synthetic datasets given by NCLRS and presented algorithm, respectively. The experimental results or biological dataset show that our approach is an effective tool to account for non-genetic effects than currently existing methods.

In recent years, a remarkable amount of protein-protein interaction (PPI) data are being available owing to the advance made in experimental high-throughput technologies. However, the experimentally detected PPI data usually contain a large amount of spurious links, which could contaminate the analysis of the biological significance of protein links and lead to incorrect biological discoveries, thereby posing new challenges to both computational and biological scientists. In this paper, we develop a new embedding algorithm called local similarity preserving embedding (LSPE) to rank the interaction possibility of protein links. By going beyond limitations of current geometric embedding methods for network denoising and emphasizing the local information of PPI networks, LSPE can avoid the unstableness of previous methods. We demonstrate experimental results on benchmark PPI networks and show that LSPE was the overall leader, outperforming the state-of-the-art methods in topological false links elimination problems.

Lysine succinylation is one of most important types in protein post-translational modification, which is involved in many cellular processes and serious diseases. However, effective recognition of such sites with traditional experiment methods may seem to be treated as time-consuming and laborious. Those methods can hardly meet the need of efficient identification a great deal of succinylated sites at speed. In this work, several physicochemical properties of succinylated sites have been extracted, such as the physicochemical property of the amino acids. Flexible neural tree, which is employed as the classification model, was utilized to integrate above mentioned features for generating a novel lysine succinylation prediction framework named ILSES (identification lysine succinylation-sites with ensemble features classification). Such method owns the ability to combining diverse features to predict lysine succinylation with high accuracy and real time.

Built upon the hypergeometric distribution, the Fisher Exact Test score (FETS) and its variants offer a natural way of quantifying the level of TF binding site (TFBS) motif enrichment, and have been chosen as the objective functions of several widely used discriminant motif discovery methods, such as HOMER and DREME. In spite of its popularity and efficacy, FETS is non-smooth and non-differentiable, and is thus difficult to optimize numerically. To circumvent this limitation, existing tools that learn to optimize FETS either have to rely on discrete search strategies or indirect tuning of a few external parameters, which could hurt accuracy and fail to fully utilize the potential of input sequences to generate motifs. In this paper, we propose DirectFS, which is (to our best knowledge) the first FETS-based approach that allows direct learning of the motif parameters in continuous space. We show that when the resultant loss function is optimized in a coordinate-wise manner, the cost function of each resultant sub-problem is a piece-wise constant function, whose optimal value can be found exactly and efficiently. Further, a key step in each iteration of DirectFS requires finding the most statistically significant one among tens of thousands of Fisher's exact tests, which is solved efficiently using a novel `lookahead'-style algorithm. Experimental evaluations on ENCODE ChIP-seq data illustrate the performance of the proposed approach.

The problems of estimating missing values in visual data appear ubiquitously in computer vision applications including image inpainting, video inpainting, hyperspectral data recovery, and magnetic resonance imaging (MRI) data recovery. Recently, it is shown that tensor completion, which generalizes matrix completion to multiway data of higher order, could accurately estimate the overall data structure and achieves state-of-the-art performance for video completion. However, current tensor-based approaches implicitly assume that the partially observed video is globally low rank, which is too stringent for practical applications where the input video could include multiple heterogeneous episodes and the global correlation between frames is not high. To tackle this problem, we propose a novel local sensitive formulation of tensor learning where we assume instead that the video is inter-correlated in a local manner, leading to a representation of the observed tensor as a weighted sum of low-rank tensors. Computationally, we also design efficient scheme for solving the resulting learning problem based on the alternating direction method of multipliers (ADMM). Our experiments show improvements in prediction accuracy over classical approaches for visual data completion tasks.

Most of current ANN represents relations in the way of functional approximation. It is good for representing the numeric relations or ratios of things. However, it is not proper to represent logical relations in the form of ratio. Therefore, aiming for representing logical relations directly, we propose a new ANN model PLDNN (Probabilistic Logical Dynamical Neural Network). It defines new neurons and multiple kinds of links (including exciting links and inhibitory links) specified for representing logical relations according to the data. Limited to the perceiving range and other factors, complete information cannot be always gotten. Incomplete information causes the uncertainty and incompletion of logical relations. To deal with it, the probabilities are assigned to the weights of links to indicate the belief degree of logical relations under the uncertain situations. PLDNN creates links on demand to build the network dynamically to represent the logical relations according to the data. It not only uses the weights of links to memorize information, but also uses the network structure to make it store more information. Dynamical network structure makes it high adaptability. The model could be used as the complement to current numeric ANN to deal with logical issues and to expand the application areas of ANN.

Cervical cancer is the third most common malignancy in women worldwide. It remains a leading cause of cancer-related death for women in developing countries. In order to contribute to the treatment of the cervical cancer, in our work, we try to find a few key genes resulting in the cervical cancer. Employing functions of several bioinformatics tools, we selected 143 differentially expressed genes (DEGs) associated with the cervical cancer. The results of bioinformatics analysis show that these DEGs play important roles in the development of cervical cancer. Through comparing two differential co-expression networks (DCNs) at two different states, we found a common sub-network and two differential sub-networks as well as some hub genes in three sub-networks. Moreover, some of the hub genes have been reported to be related to the cervical cancer. Those hub genes were analyzed from Gene Ontology function enrichment, pathway enrichment and protein binding three aspects. The results can help us understand the development of the cervical cancer and guide further experiments about the cervical cancer.

This special section includes a selection of eight papers presented at the 10th International Conference on Intelligent Computing (ICIC) held in Taiyuan, China, on August 3–6, 2014.

In recent years, thanks to the efforts of individual scientists and research consortiums, a huge amount of chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) experimental data have been accumulated. Instead of investigating them independently, several recent studies have convincingly demonstrated that a wealth of scientific insights can be gained by integrative analysis of these ChIP-seq data. However, when used for the purpose of integrative analysis, a serious drawback of current ChIP-seq technique is that it is still expensive and time-consuming to generate ChIP-seq datasets of high standard. Most researchers are therefore unable to obtain complete ChIP-seq data for several TFs in a wide variety of cell lines, which considerably limits the understanding of transcriptional regulation pattern. In this paper, we propose a novel method called ChIP-PIT to overcome the aforementioned limitation. In ChIP-PIT, ChIP-seq data corresponding to a diverse collection of cell types, TFs and genes are fused together using the three-mode pair-wise interaction tensor (PIT) model, and the prediction of unperformed ChIP-seq experimental results is formulated as a tensor completion problem. Computationally, we propose efficient first-order method based on extensions of coordinate descent method to learn the optimal solution of ChIP-PIT, which makes it particularly suitable for the analysis of massive scale ChIP-seq data. Experimental evaluation the ENCODE data illustrate the usefulness of the proposed model.

This paper considers the stochastic point location (SPL) problem as a learning mechanism trying to locate a point on a real line via interacting with a random environment. Compared to the stochastic environment in the literatures that confines the learning mechanism to moving in two directions, i.e., left or right, this paper introduces a general triple level stochastic environment which not only tells the learning mechanism to go left or right, but also informs it to stay unmoved. It is easy to understand, as we will prove in this paper, that the environment reported in the previous literatures is just a special case of the triple level environment. And a new learning algorithm, named as random walk-based triple level learning algorithm, is proposed to locate an unknown point under this new type of environment. In order to examine the performance of this algorithm, we divided the triple level SPL problems into four distinguished scenarios by the properties of the unknown point and the stochastic environment, and proved that even under the triple level nonstationary environment and the convergence condition having not being satisfied for some time, which are rarely considered in existing SPL problems, the proposed learning algorithm is still working properly whenever the unknown point is static or evolving with time. Extensive experiments validate our theoretical analyses and demonstrate that the proposed learning algorithms are quite effective and efficient.

The recently proposed family of discriminative motif finders is promising for harnessing the power of large quantities of accumulated high-throughput experimental data, however, they have to sacrifice accuracy by employing simplified statistical models during the learning process. In this paper, we propose a new approach called Discriminative Motif Learning via AUC (DiscMLA) to discover motifs on large-scale datasets. Unlike previous approaches, DiscMLA tries to optimize AUC directly during motifs searching. In addition, based on an observation, some novel processes are designed for accelerating DiscMLA. The experimental results show that our approach substantially outperforms previous methods on discriminative motif learning problems. DiscMLA' stability, discrimination and validity will help to exploit high-throughput datasets and answer many fundamental biological questions.

In recent years, thanks to the efforts of individual scientists and research consortiums, a huge amount of chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) experimental data have been accumulated. Although several recent studies have demonstrated that a wealth of insights can be gained by integrative analysis of these data, owing to cost, time or sample material availability, it is not always possible for researchers to obtain binding profiles for every proteins in every sample of interest, which considerably limits the power of integrative studies. In this paper, we propose a novel method called Low Rank Convex Co-Embedding (LRCCE) for imputing new ChIP-seq datasets. In LRCCE, a diverse collection of available ChIP-seq data are fused together by mapping proteins, samples, and genomic positions simultaneously into the Euclidean space, thereby making their underling associations directly evaluable using simple calculations. In contrast with previous approaches which mainly use of the local correlations between available datasets, LRCCE can better estimate the overall data structure by formulating the representation learning of all involved entities as a single unified optimization problem. Experimental evaluations on the ENCODE data illustrate the usefulness of the proposed model.

Extreme learning machine (ELM) is one of suitable base-classifiers for ensemble learning systems because of its fast learning speed, good generalization performance and simple setting. For the ensemble learning, how to select the base classifiers is a key issue which influences the performance of the ensemble system dramatically. To obtain a compact ensemble system with improved generalization performance, a diversity guided ensemble of ELMs based on attractive and repulsive particle swarm optimization (ARPSO) is proposed in this paper. In the proposed method, ARPSO considers both the convergence accuracy on the validation data and the diversity of the ensemble system. To effectively weigh the diversity of the ensemble system, a new diversity based on the Euclidean distance among the candidate ELMs is defined in this study. Experimental results on function approximation and benchmark classification problems verify that the proposed method could build more compact ensemble of ELMs with better generalization performance than some classical ensemble of ELMs.

MOTIVATION: MicroRNAs (miRNAs) are short non-coding RNAs that play important roles in post-transcriptional regulations as well as other important biological processes. Recently, accumulating evidences indicate that miRNAs are extensively involved in cancer. However, it is a big challenge to identify which miRNAs are related to which cancer considering the complex processes involved in tumors, where one miRNA may target hundreds or even thousands of genes and one gene may regulate multiple miRNAs. Despite integrative analysis of matched gene and miRNA expression data can help identify cancer-associated miRNAs, such kind of data is not commonly available. On the other hand, there are huge amount of gene expression data that are publicly accessible. It will significantly improve the efficiency of characterizing miRNA's function in cancer if we can identify cancer miRNAs directly from gene expression data. RESULTS: We present a novel computational framework to identify the cancer-related miRNAs based solely on gene expression profiles without requiring either miRNA expression data or the matched gene and miRNA expression data. The results on multiple cancer datasets show that our proposed method can effectively identify cancer-related miRNAs with higher precision compared with other popular approaches. Furthermore, some of our novel predictions are validated by both differentially expressed miRNAs and evidences from literature, implying the predictive power of our proposed method. In addition, we construct a cancer-miRNA-pathway network, which can help explain how miRNAs are involved in cancer. AVAILABILITY AND IMPLEMENTATION: The R code and data files for the proposed method are available at http://comp-sysbio.org/miR_Path/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: supplementary data are available at Bioinformatics online.

Chloroplasts are organelles in most green plant and some algal cells. Identifying protein subchloroplast localization in chloroplast organelle is very helpful for understanding the function of chloroplast proteins. There have existed a few computational prediction methods for protein subchloroplast localization. However, these existing works have ignored proteins with multiple subchloroplast locations when constructing prediction models, so that they can only predict one of all subchloroplast locations of this kind of multilabel proteins. To address this problem, through utilizing label-specific features and label correlations simultaneously, a novel multi-label classifier was developed for predicting protein subchloroplast location(s) with both single and multiple location sites. As an initial study, the overall accuracy of our proposed algorithm reach 55.52%, which is quite high to be able to become a promising tool for further studies.

Protein-protein interactions (PPIs) play crucial roles in the execution of various cellular processes and form the basis of biological mechanisms. Although large amount of PPIs data for different species has been generated by high-throughput experimental techniques, the data produced by these techniques have high levels of spurious interactions. Hence, it is of great practical significance to develop reliable computational methods to facilitate the identification of PPIs. In this paper, we propose a new geometric approach called Leave-One-Out Logistic Metric Embedding (LOO-LME) for assessing the reliability of interactions. Unlike previous approaches which mainly seek to preserve the noisy topological information of the PPI networks in the embedding space, LOO-LME first transforms the learning task into an equivalent discriminant form, then directly deals with the uncertainty in PPI networks using a leave-one-out-style approach. The experimental results show that LOO-LME substantially outperforms previous methods on PPI assessment problems. LOO-LME could thus facilitate further graph-based studies of PPIs and may help infer their hidden underlying biological knowledge.

The Local Binary Pattern (LBP) and its variants have been widely investigated in image processing and computer vision applications, e.g., texture classification due to their powerful ability to capture image features and computational simplicity. However, owing to the simple selection strategy of the threshold, the original LBP descriptor is sensitive to noise and illumination variations and tends to characterize different local patterns with the same binary code. Recently, the Completed Robust Local Binary Pattern (CRLBP) has been introduced to overcome these demerits, in which the Weighted Local Gray Level (WLG) is introduced to replace the traditional gray value of the center pixel, but the improvement is not significant and one additional parameter has to be tuned. To address these difficulties effectively, this paper proposes a hybrid framework of LBP, called Completed Hybrid Local Binary Pattern (CHLBP), in which a first order derivative and a second order derivative are combined to represent local patterns. In order to make CHLBP more robust and stable, more relationship information among pixels in the local region is exploited, that is, the Average Local Gray Level (ALG) is adopted to take place of the traditional gray value of the center pixel as well as the neighbor pixels. The results obtained from two representative texture databases show that the proposed method is robust to illuminant variations and viewpoint variations and can achieve impressive classification accuracy. The proposed model improves the classification results from 96.95% to 98.78% on the Outex database, and from 91.85% to 94.56% on the UIUC database as compared with the Completed Local Binary Pattern (CLBP), which is the benchmark method of LBP-based models.

The R package SFAPS has been developed for structure/function analysis of protein sequences based on information spectrum method. The informational spectrum method employs the electron-Ion interaction potential parameter as the numerical representation for the protein sequence, and obtains the characteristic frequency of a particular protein interaction after computing the Discrete Fourier Transform (DFT) for protein sequences. The informational spectrum method is often used to analyze protein sequences, so we developed this software, which is implemented as an add-on package to the freely available and widely used statistical language R. Our package is distributed as open source code for Linux, Unix and Microsoft Windows. It is released under the GNU General Public License.

Protein interactions play vital roles in biological processes. The study for protein interface will allow people to elucidate the mechanism of protein interaction. However, a large portion of protein interface data is incorrectly collected currently. In this paper, a dataset reconstruction strategy using manifold learning method has been proposed for dealing with the noises in the interaction interface data whose definition is based on the residue distances among the different chains within protein complexes. Three support vector machine-based predictors are constructed using different protein features to identify the functional sites involved in the formation of protein interface. The experimental results achieved in this work demonstrate that our strategy can remove noises, and therefore improve the ability for identification of protein interfaces with 77.8% accuracy.

Based on all kinds of adjacent amino acids (AAA), we map each protein primary sequence into a 400 by ((L-1)) matrix (M). In addition, we further derive a normalized 400-tuple mathematical descriptors (D), which is extracted from the primary protein sequences via singular values decomposition (SVD) of the matrix. The obtained 400-D normalized feature vectors (NFVs) further facilitate our quantitative analysis of protein sequences. Using the normalized representation of the primary protein sequences, we analyze the similarity for different sequences upon two data sets: 1) ND5 sequences from nine species and 2) transferrin sequences of 24 vertebrates. We also compared the results in this study with those from other related works. These two experiments illustrate that our proposed NFV-AAA approach does perform well in the field of similarity analysis of sequence.

Graphical representations provide us with a tool allowing visual inspection of the sequences. To visualize and compare different DNA sequences, a novel alignment-free method is proposed in this paper for both graphical representation and similarity analysis of sequences. We introduce a transformation to represent each DNA sequence with neighboring nucleotide matrix. Then, based on approximate joint diagonalization theory, we transform each DNA primary sequence into a corresponding eigenvalue vector (EVV), which can be considered as numerical characterization of DNA sequence. Meanwhile, we get graphical representation for DNA sequence via the plot of EVV in 2-D plane. Moreover, using k-means, we cluster these feature curves of sequences into several reasonable subclasses. In addition, similarity analyses are performed by computing the distances among the obtained vectors. This approach contains more sequence information, and it analyzes all the involved sequence information jointly rather than separately. A typical dendrogram constructed by this method demonstrates the effectiveness of our approach.

In this paper, we propose a novel inverse-free iterative algorithm for efficiently solving the generalized eigenvalue problem in Canonical Correlation Analysis (CCA). Compared with the state-of-the-art approach of reformulating it as a regression problem, our method is more efficient and can find the exact solution to the original generalized eigenvalue problem under a milder condition. Numerical experiments on several large-scale datasets illustrate the superior performance of the proposed method.

The advancement of techniques in computer science and information technology witnessed the rapid growth of bioinformatics in various diverse areas such as sequence alignment, structure prediction, structure-function relationship, protein interactions, genome annotation, gene expression, microarray data analysis and so on. It is necessary and pertinent to discuss the issues on these topics and analyze the latest developments. The International Conference on Intelligent Computing (ICIC) provided a forum for discussing the recent investigations on bioinformatics related problems using high performance computing and efficient algorithms. Among the 832 submissions 33.7% were selected for presentations at ICIC 2011. Based on the novelty of the manuscripts, presentations and originality only 12 papers were selected as 'high quality', and the extended versions of them are included in the supplement. The supplement is broadly classified into five categories, structure-function relationship of proteins, protein-protein interactions, gene expression/interaction networks, microarray data analysis and visualization tools. The opening article by Gromiha et al. [1] related various physical, chemical, energetic and conformational properties of amino acid residues with the change of half maximal effective concentration (EC50) due to amino acid substitutions in olfactory receptors. Further, they utilized machine learning methods for discriminating the mutants, which enhance or reduce EC50 values upon mutation. Wang et al. [2] proposed a protein-protein dissimilarity learning algorithm for comparing protein structures using the contextual information of proteins. Lei et al. [3] developed a robust computational technique for assessing the reliability of protein-protein interactions and predicting the interacting pairs of proteins by integrating manifold embedding with various features. Wang et al. [4] described an algorithm for identifying overlapping modules in protein-protein interaction networks. Cui et al. [5] built a support vector machine model for predicting human proteins that interact with virus proteins and specifically human papillomavirus and hepatitis C virus. Liu et al. [6] constructed an integrated map of protein interaction network in Mycobacterium tuberculosis using machine learning and ortholog-based methods. Wang et al. [7] presented a network biology approach for investigating drug combinations and their target proteins in the context of genetic interaction networks and related human pathways with an aim to understand the underlying rules of effective drug combinations. Hsiao et al. [8] proposed an incremental evolutionary approach using network robustness for inferring gene regulatory networks with an application to deal with a large number of network parameters. Bevilacqua et al. [9] explored the issue of microarray data merging and used distant metastasis prediction for classifying three different sets of breast cancer data. Park et al. [10] analyzed the whole brain microarray data and physical connectivity of hippocampus with other brain regions to identify the genes related to Alzheimer's disease and their interactions with proteins. Ayadi et al. [11] described a stochastic pattern-driven neighborhood search algorithm for biclustering microarray data. In the last article, Jung et al. [12] described the development of a JAVA based stand-alone program for detecting and visualizing of genomic variants, which enables the manual exclusion of erroneous signals. It is also capable of visualizing genomic data from different sources such as data from comparative genomic hybridization arrays and sequence alignment format files. The guest editors of the supplement would like to thank the Executive Editor of BMC Bioinformatics Professor Kate Rice for providing an opportunity to publish some of the excellent papers presented in ICIC 2011. We also wish to thank Ms. Isobel Peters and Ms. Catherine Wells for their help and support in editing the supplement. Finally, our sincere thanks to all the authors of the papers selected for publication in this issue. This work was partially supported by the grants of the National Science Foundation of China, Nos. 61133010, & 31071168.

BACKGROUND: Protein-protein interactions (PPIs) play crucial roles in virtually every aspect of cellular function within an organism. Over the last decade, the development of novel high-throughput techniques has resulted in enormous amounts of data and provided valuable resources for studying protein interactions. However, these high-throughput protein interaction data are often associated with high false positive and false negative rates. It is therefore highly desirable to develop scalable methods to identify these errors from the computational perspective. RESULTS: We have developed a robust computational technique for assessing the reliability of interactions and predicting new interactions by combining manifold embedding with multiple information integration. Validation of the proposed method was performed with extensive experiments on densely-connected and sparse PPI networks of yeast respectively. Results demonstrate that the interactions ranked top by our method have high functional homogeneity and localization coherence. CONCLUSIONS: Our proposed method achieves better performances than the existing methods no matter assessing or predicting protein interactions. Furthermore, our method is general enough to work over a variety of PPI networks irrespectively of densely-connected or sparse PPI network. Therefore, the proposed algorithm is a much more promising method to detect both false positive and false negative interactions in PPI networks.

Tumor classification based on gene expression profiles, which is of great benefit to the accurate diagnosis and personalized treatment for different types of tumor, has drawn a great attention in recent years. This paper proposes a novel tumor classification method based on correlation filters to identify the overall pattern of tumor subtype hidden in differentially expressed genes. Concretely, two correlation filters, i.e., Minimum Average Correlation Energy (MACE) and Optimal Tradeoff Synthetic Discriminant Function (OTSDF), are introduced to determine whether a test sample matches the templates synthesized for each subclass. The experiments on six publicly available datasets indicate that the proposed method is robust to noise, and can more effectively avoid the effects of dimensionality curse. Compared with many model-based methods, the correlation filter based method can achieve better performance when balanced training sets are exploited to synthesize the templates. Particularly, the proposed method can detect the similarity of overall pattern while ignoring small mismatches between test sample and the synthesized template. And it performs well even if only few training samples are available. More importantly, the experimental results can be visually represented, which is helpful for the further analysis of results.

In this brief, we propose a novel contour-based shape descriptor, called the multiscale distance matrix, to capture the shape geometry while being invariant to translation, rotation, scaling, and bilateral symmetry. The descriptor is further combined with a dimensionality reduction to improve its discriminative power. The proposed method avoids the time-consuming pointwise matching encountered in most of the previously used shape recognition algorithms. It is therefore fast and suitable for real-time applications. We applied the proposed method to the task of plan leaf recognition with experiments on two data sets, the Swedish Leaf data set and the ICL Leaf data set. The experimental results clearly demonstrate the effectiveness and efficiency of the proposed descriptor.

In this brief, a novel local descriptor, named local binary count (LBC), is proposed for rotation invariant texture classification. The proposed LBC can extract the local binary grayscale difference information, and totally abandon the local binary structural information. Although the LBC codes do not represent visual microstructure, the statistics of LBC features can represent the local texture effectively. In addition, a completed LBC (CLBC) is also proposed to enhance the performance of texture classification. Experimental results obtained from three databases demonstrate that the proposed CLBC can achieve comparable accurate classification rates with completed local binary pattern.

The algorithm of Continuous Point Location with Adaptive d-ary Search (CPL-AdS) strategy exhibits its efficiency in solving stochastic point location (SPL) problems. However, there is one bottleneck for this CPL-AdS strategy which is that, when the dimension of the feature, or the number of divided subintervals for each iteration, d is large, the decision table for elimination process is almost unavailable. On the other hand, the larger dimension of the features d can generally make this CPL-AdS strategy avoid oscillation and converge faster. This paper presents a generalized universal decision formula to solve this bottleneck problem. As a matter of fact, this decision formula has a wider usage beyond handling out this SPL problems, such as dealing with deterministic point location problems and searching data in Single Instruction Stream-Multiple Data Stream based on Concurrent Read and Exclusive Write parallel computer model. Meanwhile, we generalized the CPL-AdS strategy with an extending formula, which is capable of tracking an unknown dynamic parameter λ in both informative and deceptive environments. Furthermore, we employed different learning automata in the generalized CPL-AdS method to find out if faster learning algorithm will lead to better realization of the generalized CPL-AdS method. All of these aforementioned contributions are vitally important whether in theory or in practical applications. Finally, extensive experiments show that our proposed approaches are efficient and feasible.

A reliable and accurate identification of the type of tumors is crucial to the proper treatment of cancers. In recent years, it has been shown that sparse representation (SR) by l1-norm minimization is robust to noise, outliers and even incomplete measurements, and SR has been successfully used for classification. This paper presents a new SR-based method for tumor classification using gene expression data. A set of metasamples are extracted from the training samples, and then an input testing sample is represented as the linear combination of these metasamples by l1-regularized least square method. Classification is achieved by using a discriminating function defined on the representation coefficients. Since l1-norm minimization leads to a sparse solution, the proposed method is called metasample-based SR classification (MSRC). Extensive experiments on publicly available gene expression data sets show that MSRC is efficient for tumor classification, achieving higher accuracy than many existing representative schemes.

A reliable and precise identification of the type of tumors is crucial to the effective treatment of cancer. With the rapid development of microarray technologies, tumor clustering based on gene expression data is becoming a powerful approach to cancer class discovery. In this paper, we apply the penalized matrix decomposition (PMD) to gene expression data to extract metasamples for clustering. The extracted metasamples capture the inherent structures of samples belong to the same class. At the same time, the PMD factors of a sample over the metasamples can be used as its class indicator in return. Compared with the conventional methods such as hierarchical clustering (HC), self-organizing maps (SOM), affinity propagation (AP) and nonnegative matrix factorization (NMF), the proposed method can identify the samples with complex classes. Moreover, the factor of PMD can be used as an index to determine the cluster number. The proposed method provides a reasonable explanation of the inconsistent classifications made by the conventional methods. In addition, it is able to discover the modules in gene expression data of conterminous developmental stages. Experiments on two representative problems show that the proposed PMD-based method is very promising to discover biological phenotypes.

In this paper, an efficient feature extraction algorithm called orthogonal linear local spline discriminant embedding (O-LLSDE) is proposed for face recognition. Derived from local spline embedding (LSE), O-LLSDE not only inherits the advantages of LSE which uses local tangent space as a representation of the local geometry so as to preserve the local structure, but also makes full use of class information and orthogonal subspace to improve discriminant power. Extensive experiments on standard face databases demonstrate the effectiveness of the proposed method.

In the field of pattern recognition and machine learning, many problems are involved in the tasks of dimensionality reduction and then classification. In this paper, we develop an efficient dimensionality reduction method named MiniRisk Supervised Discrimiant Projection (MRSDP), which extracts effective low-dimensional features for classification purpose. The proposed method utilizes discriminant information to guide the procedure of extracting intrinsic low-dimensional features and provides a linear projection matrix. Since MRSDP is based on minimax risk criterion, it can minimize the maximal probability of misclassification in the common borders of different classes of data by contracting within-class scatter and maximizing between-class scatter. The advantage of our method is borne out by comparison with other widely used methods. In the experiments on Yale face database and ORL face database, our method achieves constantly superior performance than those competing methods.

Classical linear dimensional reduction algorithms, such as Linear Discriminant Analysis (LDA) and Locality Preserving Projections (LPP) have been widely used in computer vision and pattern recognition. However, when dealing with the multidimensional dataset, they usually first transform the original data to vectors, and then analyze the data in such a high dimensional space. This process inevitably results in some obvious disadvantages. This paper proposes a novel two-dimensional dimensionality reduction algorithm called 2D Neighborhood Discriminant Projection (2D-NDP), which is based directly on 2D image matrices rather than 1D vectors. 2D-NDP detects the intrinsic class-relationships between the images by incorporating both class label information and neighborhood information. It can optimally preserve not only the local class information but discriminant information as well. Under the orthogonal constrain, 2D-NDP is developed as orthogonal 2D-NDP for classification. Experiments on the face database and the plant leaf database demonstrate that orthogonal 2D-NDP is effective and feasible for classification.

MOTIVATION: High-throughput protein interaction data, with ever-increasing volume, are becoming the foundation of many biological discoveries, and thus high-quality protein-protein interaction (PPI) maps are critical for a deeper understanding of cellular processes. However, the unreliability and paucity of current available PPI data are key obstacles to the subsequent quantitative studies. It is therefore highly desirable to develop an approach to deal with these issues from the computational perspective. Most previous works for assessing and predicting protein interactions either need supporting evidences from multiple information resources or are severely impacted by the sparseness of PPI networks. RESULTS: We developed a robust manifold embedding technique for assessing the reliability of interactions and predicting new interactions, which purely utilizes the topological information of PPI networks and can work on a sparse input protein interactome without requiring additional information types. After transforming a given PPI network into a low-dimensional metric space using manifold embedding based on isometric feature mapping (ISOMAP), the problem of assessing and predicting protein interactions is recasted into the form of measuring similarity between points of its metric space. Then a reliability index, a likelihood indicating the interaction of two proteins, is assigned to each protein pair in the PPI networks based on the similarity between the points in the embedded space. Validation of the proposed method is performed with extensive experiments on densely connected and sparse PPI network of yeast, respectively. Results demonstrate that the interactions ranked top by our method have high-functional homogeneity and localization coherence, especially our method is very efficient for large sparse PPI network with which the traditional algorithms fail. Therefore, the proposed algorithm is a much more promising method to detect both false positive and false negative interactions in PPI networks. AVAILABILITY: MATLAB code implementing the algorithm is available from the web site http://home.ustc.edu.cn/∼yzh33108/Manifold.htm.

BACKGROUND: It is well known that most of the binding free energy of protein interaction is contributed by a few key hot spot residues. These residues are crucial for understanding the function of proteins and studying their interactions. Experimental hot spots detection methods such as alanine scanning mutagenesis are not applicable on a large scale since they are time consuming and expensive. Therefore, reliable and efficient computational methods for identifying hot spots are greatly desired and urgently required. RESULTS: In this work, we introduce an efficient approach that uses support vector machine (SVM) to predict hot spot residues in protein interfaces. We systematically investigate a wide variety of 62 features from a combination of protein sequence and structure information. Then, to remove redundant and irrelevant features and improve the prediction performance, feature selection is employed using the F-score method. Based on the selected features, nine individual-feature based predictors are developed to identify hot spots using SVMs. Furthermore, a new ensemble classifier, namely APIS (A combined model based on Protrusion Index and Solvent accessibility), is developed to further improve the prediction accuracy. The results on two benchmark datasets, ASEdb and BID, show that this proposed method yields significantly better prediction accuracy than those previously published in the literature. In addition, we also demonstrate the predictive power of our proposed method by modelling two protein complexes: the calmodulin/myosin light chain kinase complex and the heat shock locus gene products U and V complex, which indicate that our method can identify more hot spots in these two complexes compared with other state-of-the-art methods. CONCLUSION: We have developed an accurate prediction model for hot spot residues, given the structure of a protein complex. A major contribution of this study is to propose several new features based on the protrusion index of amino acid residues, which has been shown to significantly improve the prediction performance of hot spots. Moreover, we identify a compact and useful feature subset that has an important implication for identifying hot spot residues. Our results indicate that these features are more effective than the conventional evolutionary conservation, pairwise residue potentials and other traditional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spot residues. The data and source code are available on web site http://home.ustc.edu.cn/~jfxia/hotspot.html.

BACKGROUND: Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited for annotating gene functions and dissecting specific pathway structures. However, our understanding is rather limited to the relationship between double concurrent perturbation and various higher level phenotypic changes, e.g. those in cells, tissues or organs. Modifier screens, such as synthetic genetic arrays (SGA) can help us to understand the phenotype caused by combined gene mutations. Unfortunately, exhaustive tests on all possible combined mutations in any genome are vulnerable to combinatorial explosion and are infeasible either technically or financially. Therefore, an accurate computational approach to predict genetic interaction is highly desirable, and such methods have the potential of alleviating the bottleneck on experiment design. RESULTS: In this work, we introduce a computational systems biology approach for the accurate prediction of pairwise synthetic genetic interactions (SGI). First, a high-coverage and high-precision functional gene network (FGN) is constructed by integrating protein-protein interaction (PPI), protein complex and gene expression data; then, a graph-based semi-supervised learning (SSL) classifier is utilized to identify SGI, where the topological properties of protein pairs in weighted FGN is used as input features of the classifier. We compare the proposed SSL method with the state-of-the-art supervised classifier, the support vector machines (SVM), on a benchmark dataset in S. cerevisiae to validate our method's ability to distinguish synthetic genetic interactions from non-interaction gene pairs. Experimental results show that the proposed method can accurately predict genetic interactions in S. cerevisiae (with a sensitivity of 92% and specificity of 91%). Noticeably, the SSL method is more efficient than SVM, especially for very small training sets and large test sets. CONCLUSIONS: We developed a graph-based SSL classifier for predicting the SGI. The classifier employs topological properties of weighted FGN as input features and simultaneously employs information induced from labelled and unlabelled data. Our analysis indicates that the topological properties of weighted FGN can be employed to accurately predict SGI. Also, the graph-based SSL method outperforms the traditional standard supervised approach, especially when used with small training sets. The proposed method can alleviate experimental burden of exhaustive test and provide a useful guide for the biologist in narrowing down the candidate gene pairs with SGI. The data and source code implementing the method are available from the website: http://home.ustc.edu.cn/~yzh33108/GeneticInterPred.htm.

Microarray technology is a useful tool for monitoring the expression levels of thousands of genes simultaneously. Recently, mixture modeling has been used to extract expression signatures from gene expression profiles. In general, two separate steps are utilized to estimate the number of classes and model parameters, respectively. However, such a method is often time-consuming and leads to suboptimal solutions. In this paper, we therefore apply a one-step approach, namely Rival Penalized Expectation-Maximization (RPEM) algorithm, to analyze the gene expression data. The RPEM algorithm is capable of estimating the parameters of normal mixture model, while determining the number of classes automatically at the same time. Furthermore, we speed up the learning procedure of RPEM by proposing a new mechanism to adjust the learning rate dynamically. The numerical results on real gene expression data demonstrate that our proposed method is indeed effective and efficient.

Microarray technology has been widely applied to search for biomarkers of diseases, diagnose diseases and analyze gene regulatory network. Abundance of expression data from microarray experiments are processed by informatics tools, such as supporting vector machines (SVM), artificial neural network (ANN), and so on. These methods achieve good results in single dataset. Nevertheless, most analyses of microarray data are only focused on a series of data obtained from the same lab or gene chip. Then the discoveries may only be suitable for data they experimented on but lack of general sense. In this paper, we propose a genetic programming (GP) based approach to analyze microarray datasets. The GP implements classification and feature selection at the same time. To validate the significance of the selected genes and generated classification rules, the results are tested on different datasets obtained from different experimental conditions. The results confirm the efficiency of GP in the classification of different samples.

Attention is a critical mechanism for visual scene analysis. By means of attention, it is possible to break down the analysis of a complex scene to the analysis of its parts through a selection process. Empirical studies demonstrate that attentional selection is conducted on visual objects as a whole. We present a neurocomputational model of object-based selection in the framework of oscillatory correlation. By segmenting an input scene and integrating the segments with their conspicuity obtained from a saliency map, the model selects salient objects rather than salient locations. The proposed system is composed of three modules: a saliency map providing saliency values of image locations, image segmentation for breaking the input scene into a set of objects, and object selection which allows one of the objects of the scene to be selected at a time. This object selection system has been applied to real images and the simulation results show its effectiveness.

Data of protein-protein interactions derived from High-throughput technologies are often incomplete and fairly noisy. Therefore, it is very important to develop computational methods for predicting protein-protein interactions. A sequence-based method is proposed by combining support vector machine and a new feature representation using Geary autocorrelation. SVM model trained with Geary autocorrelation of amino acid sequence yielded the best performance with a high accuracy of 82.9% using gold standard positives (GSPs) PRS and gold standard negatives (GSNs) RRS datasets. Meanwhile, the SVM model has been successfully employed to predict the single core PPI network.

In this paper, we propose a novel palmprint retrieval scheme based on principal lines. In the proposed scheme, the principal lines are firstly extracted by the modified finite radon transform. And then, a lot of key points located in three principal lines i.e. heart line, life line and head line are detected. Finally, the palmprints are retrieved by several key points' position and direction. The results of experiments conducted on PolyU palmprint database show that the proposed scheme is feasible and has high accurate retrieval rate with fast speed.

BACKGROUND: Supervised learning and many stochastic methods for predicting protein-protein interactions require both negative and positive interactions in the training data set. Unlike positive interactions, negative interactions cannot be readily obtained from interaction data, so these must be generated. In protein-protein interactions and other molecular interactions as well, taking all non-positive interactions as negative interactions produces too many negative interactions for the positive interactions. Random selection from non-positive interactions is unsuitable, since the selected data may not reflect the original distribution of data. RESULTS: We developed a bootstrapping algorithm for generating a negative data set of arbitrary size from protein-protein interaction data. We also developed an efficient boosting algorithm for finding interacting motif pairs in human and virus proteins. The boosting algorithm showed the best performance (84.4% sensitivity and 75.9% specificity) with balanced positive and negative data sets. The boosting algorithm was also used to find potential motif pairs in complexes of human and virus proteins, for which structural data was not used to train the algorithm. Interacting motif pairs common to multiple folds of structural data for the complexes were proven to be statistically significant. The data set for interactions between human and virus proteins was extracted from BOND and is available at http://virus.hpid.org/interactions.aspx. The complexes of human and virus proteins were extracted from PDB and their identifiers are available at http://virus.hpid.org/PDB_IDs.html. CONCLUSION: When the positive and negative training data sets are unbalanced, the result via the prediction model tends to be biased. Bootstrapping is effective for generating a negative data set, for which the size and distribution are easily controlled. Our boosting algorithm could efficiently predict interacting motif pairs from protein interaction and sequence data, which was trained with the balanced data sets generated via the bootstrapping method.

Tumor clustering is becoming a powerful method in cancer class discovery. Nonnegative matrix factorization (NMF) has shown advantages over other conventional clustering techniques. Nonetheless, there is still considerable room for improving the performance of NMF. To this end, in this paper, gene selection and explicitly enforcing sparseness are introduced into the factorization process. Particularly, independent component analysis is employed to select a subset of genes so that the effect of irrelevant or noisy genes can be reduced. The NMF and its extensions, sparse NMF and NMF with sparseness constraint, are then used for tumor clustering on the selected genes. A series of elaborate experiments are performed by varying the number of clusters and the number of selected genes to evaluate the cooperation between different gene selection settings and NMF-based clustering. Finally, the experiments on three representative gene expression datasets demonstrated that the proposed scheme can achieve better clustering results.

In this paper, a new density-based clustering framework is proposed by adopting the assumption that the cluster centers in data space can be regarded as target objects in image space. First, the level set evolution is adopted to find an approximation of cluster centers by using a new initial boundary formation scheme. Accordingly, three types of initial boundaries are defined so that each of them can evolve to approach the cluster centers in different ways. To avoid the long iteration time of level set evolution in data space, an efficient termination criterion is presented to stop the evolution process in the circumstance that no more cluster centers can be found. Then, a new effective density representation called level set density (LSD) is constructed from the evolution results. Finally, the valley seeking clustering is used to group data points into corresponding clusters based on the LSD. The experiments on some synthetic and real data sets have demonstrated the efficiency and effectiveness of the proposed clustering framework. The comparisons with DBSCAN method, OPTICS method, and valley seeking clustering method further show that the proposed framework can successfully avoid the overfitting phenomenon and solve the confusion problem of cluster boundary points and outliers.

A modified version for semi-supervised learning algorithm with local and global consistency was proposed in this paper. The new method adds the label information, and adopts the geodesic distance rather than Euclidean distance as the measure of the difference between two data points when conducting calculation. In addition we add class prior knowledge. It was found that the effect of class prior knowledge was different between under high label rate and low label rate. The experimental results show that the changes attain the satisfying classification performance better than the original algorithms.

In this paper, an efficient feature extraction method named as Constrained Maximum Variance Mapping (CMVM) is developed for dimensionality reduction. The proposed algorithm can be viewed as a linear approximation of multi-manifolds based learning approach, which takes the local geometry and manifold labels into account. After the local scatters have been characterized, the proposed method focuses on developing a linear transformation that can maximize the distances matrix between all the manifolds under the constraint of locality preserving. Then, YALE face database, ORL face database are all taken to examine the effectiveness and efficiency of the proposed method. Experimental results validate that the proposed approach is superior to other widely used feature extraction methods.

ISOMAP is a manifold learning based algorithm for dimensionality reduction, which is successfully applied to data visualization. However, there exists such limitation in classical ISOMAP that the algorithm is sensitive to noises, especially outliers. So in this paper an extended ISOMAP algorithm is put forward to solve the problem of sensitivity. The proposed algorithm follows the method of classical ISOMAP except that a preprocessing strategy is introduced to remove the noises and outliers. The likelihood of each point to be a noise or an outlier is quantified by carrying out weighted principal component analysis and box statistics method is adopted to distinguish clear points from noisy ones, then ISOMAP can be performed after de-noising. Experiments on noisy s-curve and noisy Swiss-roll data validate its efficiency for improving robustness.

In this paper, we propose a novel approach for palmprint identification, which contains two interesting components. Firstly, we propose the directional representation for appearance based approaches. The new representation is robust to drastic illumination changes and preserves important discriminative information for classification. We then generate virtual samples to enlarge the training set to compensate for matching errors caused by large rotations and translations. Based on these two strategies, the recognition performance of representative appearance based approaches can be improved significantly. Secondly, in order to improve the robustness of palmprint identification, we propose a fusion method combining proposed method and orientation based approaches e.g. Competitive Code, which can obtain very low Equal Error Rates.

In this paper, a novel heuristic structure optimization methodology for radial basis probabilistic neural networks (RBPNNs) is proposed. First, a minimum volume covering hyperspheres (MVCH) algorithm is proposed to select the initial hidden-layer centers of the RBPNN, and then the recursive orthogonal least square algorithm (ROLSA) combined with the particle swarm optimization (PSO) algorithm is adopted to further optimize the initial structure of the RBPNN. The proposed algorithms are evaluated through eight benchmark classification problems and two real-world application problems, a plant species identification task involving 50 plant species and a palmprint recognition task. Experimental results show that our proposed algorithm is feasible and efficient for the structure optimization of the RBPNN. The RBPNN achieves higher recognition rates and better classification efficiency than multilayer perceptron networks (MLPNs) and radial basis function neural networks (RBFNNs) in both tasks. Moreover, the experimental results illustrated that the generalization performance of the optimized RBPNN in the plant species identification task was markedly better than that of the optimized RBFNN.

In this paper, a novel model of elliptical basis function neural networks (EBFNN) based on a hybrid optimization algorithm is proposed. Firstly, a geometry analytic algorithm is applied to construct the hyper-ellipsoid units of hidden layer of the EBFNN, i.e., an initial structure of the EBFNN, which is further pruned by the particle swarm optimization (PSO) algorithm. And the shape parameters of kernel function for the hidden layer are also optimized by the PSO simultaneously. Finally, the hybrid learning algorithm (HLA) is further applied to adjust the hidden centers and the shape parameters of kernel function for the hidden layer. The experimental results demonstrated the proposed hybrid optimization algorithm for the EBFNN model is feasible and efficient, and the EBFNN is not only parsimonious but also has better generalization performance than the RBFNN.

It is a challenging task to diagnose tumor type precisely based on microarray data because the number of variables p (genes) is far larger than that of samples, n. Many independent component analysis (ICA) based models had been proposed to tackle the microarray data classification problem with great success. Although it was pointed out that different independent components (ICs) are of different biological significance, up to now, it is still far from well explored for the problem that how to select proper IC subsets to predict new samples best. We try to improve the performance of ICA based classification models by using proper IC subsets instead of all the ICs. A genetic algorithms (GA) based selection process is proposed in this paper, and the selected IC subset is evaluated by the leave-one-out cross validation (LOOCV) technique. The experimental results demonstrate that our GA based IC selection method can further improve the classification accuracy of the ICA based prediction models.

Microarray data prediction is a hard task due to the small sample and high dimension property. This paper proposes a classifier fusion approch to solve this problem based on genetic algorithm (GA). In this fusion strategy, GA is applied to select proper feature subsets and weight value for the fusion of classifiers. The experimental results show that the proposed scheme can improve the prediction accuracy.

This paper presents a novel multi-sub-swarm Particle Swarm Optimization (PSO) algorithm. The proposed algorithm can effectively imitate a natural ecosystem, in which the different sub-populations can compete with each other. After competing, the winner will continue to explore the original district, while the loser will be obliged to explore another district. Four benchmark multimodal functions of varying difficulty are used as test functions. The experimental results show that the proposed method has a stronger adaptive ability and a better performance for complicated multimodal functions with respect to other methods.

In this paper, an evolutionary modular neural network is proposed to solve multi-class problems with unbalanced training sets. The proposed model can transform an unbalanced classification problem into a set of symmetrical two-class problems, each of which can be solved by a single simple neural network. The experimental results show that the proposed method reduces time consumption for training and improves the classification performance.

Theoretic study in this paper shows that we can obtain exact long-range contacts by adopting one classifier if the centers of sequence profiles of residue pairs for long-range contacts and non-long-range contacts are known. The adopted classifier, referred to as multiple conditional probability mass function classifier (MCPMFC), can find an optimized transformation of the variables for each of the classes and therefore resulting in K separate classifiers. As a result, about 44.48% long-range contacts are around at the sequence profile (SP) centre for long-range contacts and about 20.9% long-range contacts are correctly predicted when considering the top L/5 (L is the protein sequence length) predicted contacts and the residue pair with 24 apart. The highest cluster result gives us a clue that SP center should be a sound pathway to investigate contact map in protein structures.

In palmprint recognition field, orientation based approaches are thought to achieve the best results in terms of recognition rates. In this paper, we propose a novel orientation based scheme, in which three strategies, the modified finite Radon transform, enlarged training set and pixel to area matching, have been designed to further improve its performance. The experimental results of verification conducted on Hong Kong Polytechnic University Palmprint Database show that our approach has higher recognition rates and faster processing speed.

This paper presents a new approach based on HMM/ANN hybrid for online signature verification. A group of ANNs are used as local probability estimators for an HMM. The Viterbi algorithm is employed to work out the global posterior probability of a model. The proposed HMM/ANN hybrid has a strong discriminant ability, i.e, from a local sense, the ANN can be regarded as an efficient classifier, and from a global sense, the posterior probability is consistent with that of a Bayes classifier. Finally, the experimental results show that this approach is promising and competing.

In this paper, self-organizing map (SOM) is used to visualize and cluster the data set of aerosol single particle mass spectrum, which was collected by aerosol time-of-flight mass spectrometry (ATOFMS). In view of the characteristic feature of aerosol particle data, the TF-IDF scheme used widely in document clustering is employed to preprocess. Subsequently for data clustering analysis, a two-level clustering framework is proposed, wherein SOM is firstly used to cluster input data and get the primary results, and then the results are again clustered by semiautomatic k-means algorithm. In order to demonstrate the validity of clustering, the chemical significance for cluster centroid is also investigated, wherein inorganic salts, "calcium-containing" particles, biogenic soot particles, and carbonaceous particles etc. are identified.

Many computational methods have been proposed for inference of protein-protein interactions as protein-protein interaction plays an important role in many cellular processes. One of methods is to infer protein-protein interactions based on domain-domain interactions, and the preliminary results have represented their feasibility. In this paper, we use the neural networks for predicting the strengths of protein interaction. This method is capable of exploring all possible interactions between domains and make predictions based on all the domains. Compared to expectation-maximization method and association method, the experimental results show that the proposed schemes can infer strengths of protein-protein interactions with better performances.

In this letter, a standard postnonlinear blind source separation algorithm is proposed, based on the MISEP method, which is widely used in linear and nonlinear independent component analysis. To best suit a wide class of postnonlinear mixtures, we adapt the MISEP method to incorporate a priori information of the mixtures. In particular, a group of three-layered perceptrons and a linear network are used as the unmixing system to separate sources in the postnonlinear mixtures, and another group of three-layered perceptron is used as the auxiliary network. The learning algorithm for the unmixing system is then obtained by maximizing the output entropy of the auxiliary network. The proposed method is applied to postnonlinear blind source separation of both simulation signals and real speech signals, and the experimental results demonstrate its effectiveness and efficiency in comparison with existing methods.

Constrained independent component analysis (cICA) is a general framework to incorporate a priori information from problem into the negentropy contrast function as constrained terms to form an augmented Lagrangian function. In this letter, a new improved algorithm for cICA is presented through the investigation of the inequality constraints, in which different closeness measurements are compared. The utility of our proposed algorithm is demonstrated by the experiments with synthetic data and electroencephalogram (EEG) data.

This paper analyzes the long-range interactions, which plays a fundamental and important role in many biologic fields, between residues in protein using principal component analysis (PCA). Firstly, one angular coordinate system of long-range interaction regions is constructed conveniently. Afterwards, a matrix of the angular values of residues can be analyzed by principal component analysis technique. Projecting the angular matrix onto its eigenvectors, it can be found that the projection is to satisfy Boltzmann distribution. By analyzing the thermodynamic environment of the interaction region and scaling the interaction regions, it can be concluded that the distribution of long-range interactions may also be obtained and as a result applied in prediction of contact map.

Identifying protein-protein interaction sites is crucial for understanding of the principles of biological systems and processes, as well as mutant design. This paper describes a novel method that can predict protein interaction sites in heterocomplexes using information of evolutionary conservation and spatial sequence profile. A predictor was generated to distinguish the interface residues from protein surface region by radial basis neural networks, which is trained by expectation maximization algorithm. Based on a non-redundant data set of heterodimers consisting of 75 protein chains, the efficiency and the effectiveness of our proposed approach can be validated by a better performance such as the accuracy of 0.60, the sensitivity of 58.3% and the specificity of 59.9%.

MOTIVATION: Microarrays are capable of determining the expression levels of thousands of genes simultaneously. One important application of gene expression data is classification of samples into categories. In combination with classification methods, this technology can be useful to support clinical management decisions for individual patients, e.g. in oncology. Standard statistic methodologies in classification or prediction do not work well when the number of variables p (genes) far too exceeds the number of samples n. So, modification of existing statistical methodologies or development of new methodologies is needed for the analysis of microarray data. RESULTS: This paper proposes a new method for tumor classification using gene expression data. In this method, we first employ independent component analysis to model the gene expression data, then apply optimal scoring algorithm to classify them. Further speaking, this approach can first make full use of the high-order statistical information contained in the gene expression data. Second, this approach also employs regularized regression models to handle the situation of large numbers of correlated predictor variables. Finally, the predictive models are developed for classifying tumors based on the entire gene expression profile. To show the validity of the proposed method, we apply it to classify four DNA microarray datasets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible. AVAILABILITY: Matlab scripts are available on request.

In this paper, a new effective model is proposed to forecast how long the postoperative patients suffered from non-small cell lung cancer will survive. The new effective model which is based on the extreme learning machine (ELM) and principal component analysis (PCA) can forecast successfully the postoperative patients' survival time. The new model obtains better prediction accuracy and faster convergence rate which the model using backpropagation (BP) algorithm and the Levenberg-Marquardt (LM) algorithm to forecast the postoperative patients' survival time can not achieve. Finally, simulation results are given to verify the efficiency and effectiveness of our proposed new model.

International Journal of Pattern Recognition and Artificial IntelligenceVol. 20, No. 02, pp. 111-112 (2006) SPECIAL ISSUE: Intelligent Computing and Applications; Edited by Y. Y. Tang and D.-S. HuangNo AccessEDITORIALYUAN YAN TANG and DE-SHUANG HUANGYUAN YAN TANGDepartment of Computer Science, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong, China and DE-SHUANG HUANGInstitute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei Anhui 230031, Chinahttps://doi.org/10.1142/S0218001406004545Cited by:0 Next AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetails Recommended Vol. 20, No. 02 Metrics History PDF download

This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well. In this study, it is achieved through simultaneous network structure determination and parameter optimization on the continuous parameter space. This is a mixed integer hard problem and the proposed HFA tackles this problem using an integrated analytic framework, leading to significantly improved network performance and reduced memory usage for the network construction. The computational complexity analysis confirms the efficiency of the proposed algorithm, and the simulation results demonstrate its effectiveness.

This paper proposes a new method for denoising natural images using our extended non-negative sparse coding (NNSC) neural network shrinkage algorithm, which is self-adaptive to the statistic property of natural images. The basic principle of denoising using NNSC shrinkage is similar to that using standard sparse shrinkage and wavelet soft threshold. Using test images corrupted by additive Gaussian noise, we evaluated the method across a range of noise levels. We utilized the normalized mean squared error as a measure of the quality of denoising images and the signal to noise rate (SNR) value as an evaluative feature of different denoising approaches. The experimental results prove that the NNSC shrinkage certainly is effective in image denoising. Otherwise, we also compare the effectiveness of the NNSC shrinkage with sparse coding shrinkage and wavelet soft threshold method. The simulative tests show that our denoising method outperforms any other of the two kinds of denoising approaches.

This paper presents a novel method to solve the protein's three-dimensional structure prediction problem. It is a machine learning approach by integrating probabilistic neural network (PNN) with conformational energy function (CEF) based on chemico-physical knowledge of amino acids. In this method, firstly, the principal components are extracted from selected protein structures with lower sequence identity, and an initial matrix of contact map is constructed by K-L expansion. Secondly, PNN is used for predicting the long-range interaction of amino acids in protein. In particular, this method uses the CEF and chemico-physical characteristics of amino acids to run the PNN predictor. Consequently, it was found that our proposed method is better than existing methods, such as the hybrid method of HMMSTR and the correlated mutation analysis method. As a result, this method can accurately predict 31% of contacts at a distance cutoff of 8/spl Aring/ for proteins whose sequence length is up to 200.

In this paper a new method for recognition of 2D occluded shapes based on neural network using generalized differential evolution training algorithm is proposed. Firstly, a generalized differential evolution (GDE) algorithm is introduced. And this GDE algorithm is applied to train multilayer perceptron neural networks. Then a new shape feature, refer to as multiscale Fourier descriptors (MFDs) is proposed. Finally, the superiority of GDE training method over traditional approaches to train networks is demonstrated by experiment. The experimental results show that our proposed GDE training method is much efficient and effective. And they also showed that the MFDs method is suitable for the shape recognition.

This paper presents a novel BP-based image retrieval (BPBIR) system, which is based on the observation that the images users need are often similar to a set of images with the same conception instead of one query image and the assumption that there is a nonlinear relationship between different features. If users aren't satisfied with the retrieved results, relevance feedback method is used to enhance the performance of the proposed system by changing the weights of the BP neural networks. In addition, we discuss some divisional methods to give rough information on the spatial color composition. Finally, we compare the performance of the proposed system with other systems. Experimental results show the efficacy of the proposed system.

In this paper, an algorithm using evolved regular expressions to characterize and predict human gene splice sites without any prior knowledge is described. In contrast to previous pattern-based approaches to the splice site detection problem, the patterns to be matched are unknown in advance and discovered using a supervised learning approach. We have used a genetic programming based system, PerlGP, to evolve regular expressions and proper length windows for a long sequence in which the evolved regular expressions can effectively characterize and predict the splice junctions. Since the gene splicing process is too complex to be fully understood currently, and the widely accepted consensus sequences only reflect some partial statistical information around splice sites, not to mention defining a splice site. However, our evolved regular expressions may shed new light on the underlying rules that define splice sites. Experimental results demonstrate that using the evolved regular expressions, splice junctions could be accurately characterized, furthermore, these evolved regular expressions could also be employed as a predictor to detect whether a CT/AG containing sequence is a splice site or not. Our experimental results also exhibit that the performance of this approach for predicting human gene splice junctions is competitive compared with some other traditional methods.

This paper proposes an extended non-negative sparse coding (NNSC) neural network method for image compression. This method can exploit the NNSC algorithm to obtain transform-based compression schemes adapted to standard natural image classes, which results from the statistical properties of natural image data. In particular, several methods of image compression such as linear principal component analysis (PCA), wavelet-based analysis, independent component analysis (ICA), etc., are evaluated and compared based on both the standard signal to noise ratio (SNR) and picture quality scale (PQS) criteria. The simulation results show that, in the case of using a fixed block by block scanning a natural image randomly, the quality of a compressed image obtained by our extended NNSC compression algorithm indeed outperforms the one obtained by other algorithms mentioned above.

In this paper, a nonlinear blind source separation system with post-nonlinear mixing; model, and an unsupervised learning algorithm for the parameters of this separating system are presented for blind inversion of Wiener system for single source. The proposed method firstly changes the deconvolution part of Wiener system into a special case of linear blind source separation (BSS). Then the nonlinear BSS system is applied to derive the source signal. The proposed nonlinear BSS method can dynamically estimate the nonlinearity of mixing model and adapt to the cumulative probability function (CPF) of sources. Finally, experimental results demonstrate that our proposed method is effective and efficient for the problems addressed.

This paper proposes a new method that can predict the interactions between proteins intermediated by the protein-domain relations. We utilize the lazy expectation maximization (LEM) to compute an improved maximization likelihood estimation (MLE) model. The protein-domain relationships are extruded from Flam database and the combined data set of Uetz and Ito are used as the source of protein-protein interactions. Finally, the efficiency and the effectiveness of our proposed approach can be validated by a better performance such as the sensitivity of 80.1%, the specificity of 43.5%, and the lesser computational cost.

In this paper an efficient shape matching approach based on fuzzy discrete particle swarm optimization (FDPSO) is proposed. Based on fuzzy theory and PSO method, we applied this optimization method to a special combinatorial optimization problem: shape matching and recognition. Firstly, an original shape is approximated to a polygone and a shape representation of invariant attributes sequence is used. Then fuzzy matrices were adopted to represent the position and velocity of the particles in PSO. Finally, the superiority of our proposed method over traditional approaches to shape matching is demonstrated by experiments. The experimental results showed that our proposed method can achieve good results due to its robustness.

Lidar is an active remote sensing instrument, but its effective range is often limited by signal-to-noise (SNR) ratio. The reason is that noises or fluctuations always strongly affect the measured results. To resolve this problem, a novel approach of using least-squares support vector machine (LS-SVM) to reconstruct the Lidar signal is proposed in this paper. LS-SVM has been proven as robust to noisy data; the Lidar signal, which is strongly corrupted by noises or fluctuations, can be thought as a function of distance. So detecting Lidar signals from high noisy regime can be regarded as a robust regression procedure which involves estimating the underlying relationship from detected signal data set. To apply the LS-SVM on Lidar signal regression, firstly the noises in Lidar signal is analyzed and then the traditional LS-SVM algorithm is modified to incorporate the a priori knowledge of the Lidar signal in the training of LS-SVM. The experimental results demonstrate the effectiveness and efficiency of our approach.

In this letter, a new nonlinear approach based on a combination of the fuzzy c-means clustering (FCMC), feature vector selection and principal component analysis (PCA) is proposed to extract features of multispectral images when a very large number of samples need to be processed. The main contribution of this letter is to provide a preprocessing method for classifying these images with higher accuracy compared to the single PCA and kernel PCA. Finally, some experimental results demonstrate that our proposed approach is effective and efficient in analyzing multispectral images.

The noise in a Lidar signal is analyzed first, and then a novel method that applies least-square support vector machine (LS-SVM) to denoising Lidar signals is proposed. In order to improve the performance of denoising, the a priori knowledge about Lidar signals is also incorporated in the training of the LS-SVM. Finally, the experimental results demonstrate the effectiveness and efficiency of our approach.

This paper proposes new modified constrained learning neural root finders (NRFs) of polynomial constructed by backpropagation network (BPN). The technique is based on the relationships between the roots and the coefficients of polynomial as well as between the root moments and the coefficients of the polynomial. We investigated different resulting constrained learning algorithms (CLAs) based on the variants of the error cost functions (ECFs) in the constrained BPN and derived a new modified CLA (MCLA), and found that the computational complexities of the CLA and the MCLA based on the root-moment method (RMM) are the order of polynomial, and that the MCLA is simpler than the CLA. Further, we also discussed the effects of the different parameters with the CLA and the MCLA on the NRFs. In particular, considering the coefficients of the polynomials involved in practice to possibly be perturbed by noisy sources, thus, we also evaluated and discussed the effects of noises on the two NRFs. Finally, to demonstrate the advantage of our neural approaches over the nonneural ones, a series of simulating experiments are conducted.

Support vector machines (SVMs) have been extensively used. However, it is known that SVMs face difficulty in solving large complex problems due to the intensive computation involved in their training algorithms, which are at least quadratic with respect to the number of training examples. This paper proposes a new, simple, and efficient network architecture which consists of several SVMs each trained on a small subregion of the whole data sampling space and the same number of simple neural quantizer modules which inhibit the outputs of all the remote SVMs and only allow a single local SVM to fire (produce actual output) at any time. In principle, this region-computing based modular network method can significantly reduce the learning time of SVM algorithms without sacrificing much generalization performance. The experiments on a few real large complex benchmark problems demonstrate that our method can be significantly faster than single SVMs without losing much generalization performance.

The spatial ordering information of amino acid residues in a protein primary sequence is an important factor in the determination of protein three-dimensional structure (tertiary structure). In this paper, we describe a radial basis function neural network, whose hidden centers and radial basis function widths are optimized by a genetic algorithm, for the purpose of predicting three dimensional spatial distance location from primary sequence information. Experimental evidence on soybean protein sequences indicates the utility of this approach.

This paper presents a comparison of three kinds of principal component neural networks, which are used for image compression. Principal component analysis (PCA), which is a statistical processing technique, is used in many engineering and scientific fields. Application of computing principal components includes data compression, pattern recognition and signal processing, etc. In this paper we use it for image compression.

In this paper, using a fixed-point theorem and reduction to absurdity, the authors have obtained some sufficient conditions to guarantee that Cohen-Grossberg neural networks with discrete and distributed delays are globally exponentially stable. Since the model is more general and the assumptions relax the previous assumptions in some existing works, the results presented in this paper are the improvement and extension of the existed ones. Finally, the validity and performance of the results are illustrated by two simulation examples.

In this paper, based on the 340 protein sequences and their corresponding secondary structures got from the protein data bank (PDB), we group the 20 different amino acids into f (former), b (breaker) and n (neutral) according to their occurring frequencies in the three-state secondary structures (/spl alpha/-helix, /spl beta/-sheets and coil), which reflect the intrinsic preference of that amino acid for a given type of secondary structure. Then we use this information to improve the protein secondary structure prediction (SSP) accuracy and get a better performance than the previous methods.

The key to mixed pixels unmixing is to determine a set of spectral endmembers that are representative of the surface components in the area covered by multispectral images. We propose a geometric method utilizing the spectrum of two or three bands to determine the endmembers under the assumption that the endmembers are the pure surface components that lie in the extremities of the scatter plot of 2- or 3-band reflectance. If there are more bands than endmembers, no additive noise and signature spectral matrix S has full column rank, we can estimate the endmemher abundances by unconstrained least squares method. Finally, the validity and feasibility of the algorithm are demonstrated by simulation results.

In this paper, we propose a new representation of DNA sequences, which constructs the word frequency vector with multiple resolutions based on the chaos game representation. Compared with the traditional vector, it combines a range of resolutions and reserves higher resolutions, but the dimension is reduced greatly relatively. The algorithm is detailed, which calculates coding format and codes each sequence. To evaluate the significance of our method, we represent Alu sequences by our proposed coding format. After that, the acquired vectors are used to train BP neural networks to recognize the Alu sequences. The experimental results show that this representation of DNA sequences is significant and efficient in biological data processing.

The LS-SVM ensemble is proposed to improve the performance of the single LS-SVM. During the constructing of the LS-SVM ensemble, bagging algorithm is used because it is more suitable than boosting algorithm in high noise regime. Furthermore, in This work a novel aggregation method of the LS-SVM ensemble is also proposed. Traditionally the aggregation of the ensemble always uses all the available individual LS-SVM, while our approach can exclude the ones which may degrade the performance of the ensemble. Finally, the simulating results demonstrate the effectiveness and efficiency of our approach.

This work presents a SVM and feature/spl I.bar/core-based algorithm for identification of key genome features. The significant difficulty in selecting key features from a high dimensional space of features is the curse of dimensions in searching. For this reason, a feature/spl I.bar/core-based search strategy is proposed in this algorithm. The strategy integrates the forward selection and backward elimination techniques. In this algorithm all key genome features are formed through the agglomeration and expansion of the feature core based on potential information about relevance in a SVM classifier. The application given proves that the algorithm is faster and more efficient than other methods such as clustering, and the single SVM-based method.

In this paper, some sufficient conditions are obtained to guarantee that the n-dimensional cellular neural networks can have even (/spl les/2/sup n/) memory patterns. And we have obtained the estimates of attracting domain of such stable memory patterns. Those conditions directly derived from the parameters of the neural networks, are very easy to verified. A new design algorithm for cellular neural networks is developed based on stability theory (not base on the well-known perceptron training algorithm), and the convergence of the design algorithm is guaranteed by some stability theorems. The results presented in this paper are new. Finally, the validity and performance of the results are illustrated by simulation results.

Genetic algorithm (GA) is one of the important and successful approaches in multiple sequences alignment (MSA) problem. In this paper, we propose an improved GA method, multiple small-popsize initialization strategy (MSPIS) and hybrid one-point crossover scheme (HOPCS) based GA, which can search the solution space in a very efficient manner. The experimental results show that our improved approach can obtain a better result compared with traditional GA approach in aligning multiple protein sequences problem.

The spatial distance of amino acids in a protein sequence is one of the important factors, which determine the three-dimension structure (tertiary structure). In this paper, we describe a genetic algorithm (GA) based radial basis function neural networks (RBFNN), whose hidden centers and radial basis function widths is optimized by the GA, to learn how primary structure (residue sequence) affects the spatial proximity of the amino acids in the soybean protein sequences and predict the residues spatial distance in three-dimensional space. Experimental results indicate that the proposed network has a good performance in soybean protein sequences residue spatial distance prediction.

When performing classification of large set of samples, neural tree classifiers (NTs) are preferred. However, the classical NTs have poor generalization properties. So, in this paper we propose a new classification method referred to as clustering-neural tree classifier, combining clustering technique with neural networks. It can be well applied to classifications of large set of samples, while having good generalization properties. The experimental results on the two spirals problem and the iris problem show that our proposed NN-tree classifier is effective and efficient.

This paper presents new theoretical results on global exponential stability of cellular neural networks with time-varying delays. The stability conditions depend on external inputs, connection weights and delays of cellular neural networks. Using these results, global exponential stability of cellular neural networks can be derived, and the estimate for location of equilibrium point can also be obtained. Finally, the simulating results demonstrate the validity and feasibility of our proposed approach.

This paper discusses using genetic algorithms (GA) to optimize the structure of radial basis probabilistic neural networks (RBPNN), including how to select hidden centers of the first hidden layer and to determine the controlling parameter of Gaussian kernel functions. In the process of constructing the genetic algorithm, a novel encoding method is proposed for optimizing the RBPNN structure. This encoding method can not only make the selected hidden centers sufficiently reflect the key distribution characteristic in the space of training samples set and reduce the hidden centers number as few as possible, but also simultaneously determine the optimum controlling parameters of Gaussian kernel functions matching the selected hidden centers. Additionally, we also constructively propose a new fitness function so as to make the designed RBPNN as simple as possible in the network structure in the case of not losing the network performance. Finally, we take the two benchmark problems of discriminating two-spiral problem and classifying the iris data, for example, to test and evaluate this designed GA. The experimental results illustrate that our designed GA can significantly reduce the required hidden centers number, compared with the recursive orthogonal least square algorithm (ROLSA) and the modified K-means algorithm (MKA). In particular, by means of statistical experiments it was proved that the optimized RBPNN by our designed GA, have still a better generalization performance with respect to the ones by the ROLSA and the MKA, in spite of the network scale having been greatly reduced. Additionally, our experimental results also demonstrate that our designed GA is also suitable for optimizing the radial basis function neural networks (RBFNN).

This letter proposes a novel neural root finder based on the root moment method (RMM) to find the arbitrary roots (including complex ones) of arbitrary polynomials. This neural root finder (NRF) was designed based on feedforward neural networks (FNN) and trained with a constrained learning algorithm (CLA). Specifically, we have incorporated the a priori information about the root moments of polynomials into the conventional backpropagation algorithm (BPA), to construct a new CLA. The resulting NRF is shown to be able to rapidly estimate the distributions of roots of polynomials. We study and compare the advantage of the RMM-based NRF over the previous root coefficient method—based NRF and the traditional Muller and Laguerre methods as well as the mathematica roots function, and the behaviors, the accuracies of the resulting root finders, and their training speeds of two specific structures corresponding to this FNN root finder: the log σand the σ FNN. We also analyze the effects of the three controlling parameters {δP0 θp η} with the CLA on the two NRFs theoretically and experimentally. Finally, we present computer simulation results to support our claims.

This paper proposes a constructive approach for finding arbitrary (real or complex) roots of arbitrary (real or complex) polynomials by multilayer perceptron network (MLPN) using constrained learning algorithm (CLA), which encodes the a priori information of constraint relations between root moments and coefficients of a polynomial into the usual BP algorithm (BPA). Moreover, the root moment method (RMM) is also simplified into a recursive version so that the computational complexity can be further decreased, which leads the roots of those higher order polynomials to be readily found. In addition, an adaptive learning parameter with the CLA is also proposed in this paper; an initial weight selection method is also given. Finally, several experimental results show that our proposed neural connectionism approaches, with respect to the nonneural ones, are more efficient and feasible in finding the arbitrary roots of arbitrary polynomials.

We propose a new approach by combining support vector clustering (SVC) with a two layered neural network for multiclass classification problems. This new approach is not only capable of improving the performance of the traditional SVC, but also, compared to support vector machines (SVM), can reduce the computational complexity and get better performance in the case of multiclass problems. Finally, the experimental results demonstrate the effectiveness and efficiency of our approach.

This paper focuses on how to find the maximum modulus root (MMR) (real or complex) of an arbitrary polynomial. Efficient solution to this problem is important for many fields including neural computation and digital signal processing etc. We present neural networks technique for solving this problem. Our neural root finder (NRF) is designed based on partitioning feedforward neural networks (FNN) trained with a constrained learning algorithm (CLA) by imposing the a priori information about the root moment from polynomial into the error cost function. Experimental results show that this neural root-finding method is able to find the maximum modulus roots of polynomials rapidly and efficiently.

In this paper, a novel human face recognition method based on radial basis probabilistic neural network (RBPNN) is proposed. The orthogonal least square algorithm (OLSA) is used to train the RBPNN and the recursive OLSA is adopted to optimize the structure of the RBPNN. The Olivetti Research Laboratory (ORL) facial database, which is preprocessed by wavelet transformation, is used to test the proposed approach. The experimental results show that the RBPNN achieves higher recognition rate and better classification efficiency with respect to radial basis function neural network (RBFNN) and BP neural network.

In this paper, a genetic algorithm (GA) is introduced into optimizing the radial basis probabilistic neural networks (RBPNN). The encoding method proposed in this paper involves not only the number and the locations of selected hidden centers but also the shape parameter of the Gaussian kernel function. We use the telling-two-spirals-apart problem as an example to validate the genetic algorithm for optimizing the RBPNN. Consequently, we obtain an optimal interval of the shape parameter of the kernel function for this problem except the reduced RBPNN structure (including the optimal number of the hidden centers and their optimal locations). The experimental results show that with the shape parameters in the optimal interval and with the optimized hidden centers the designed network is not only parsimonious but also of better generalization performance.

This paper revisits the constrained learning algorithm (CLA) proposed by Perantonis and Karras (1995), and makes further analyses and discussions on the parameters with the CLAs. Specifically, we investigate the effect of removing the constrained condition of the weight change on the CLAs. It is suggested that for those problems that do not need to do precise computation, the modified CLA is a better choice. Finally, some simulation results are presented to support our claims.

This paper proposed a partitioning neural root finder (PNRF) to find the minimum modulus (real or complex) roots of an arbitrary polynomial by imposing a minimum m order root moment (RM) into the constrained learning algorithm (CLA), where the constraint "the minimum m order RM" will ensure the minimum modulus root to be obtained. If the PNRF is recursively updated, the ordered roots from minimum modulus to maximum one can be achieved. Simulations show that this partitioning neural root-finding method is indeed able to find the minimum modulus root and the ordered roots of arbitrary polynomials readily and efficiently.

The paper focuses on discussing how to train and optimize the radial basis probabilistic neural network (RBPNN) structure by Recursive Orthogonal Least Squares Algorithms (ROLSA) combined with Micro-Genetic Algorithms (/spl mu/-GA). First, the previous ROLSA, used for optimally selecting the hidden centers of the RBPNN, was improved in two aspects, i.e., adopting new double error criterions and new stop condition. Secondly, the micro-genetic algorithm, used for optimizing the controlling parameter of kernel function, was incorporated in the improved ROLSA in order that the structure of RBPNN can be entirely optimized. Finally, to demonstrate the power of our approach, two examples, i.e., both two spirals classification problem and IRIS classification problem, were employed to validate the performance of the classification. The experimental result showed that, for the two spirals problem the structure of the RBPNN with 200 initial hidden centers was considerably compressed into the one with 30 hidden centers, and for the IRIS classification problem only 9 hidden centers among 75 initial hidden centers were selected for the optimized RBPNN structure. Whereas for the Radial Basis Function Neural Network (RBFNN), under the same condition, for the two spirals problem, there were still 46 hidden centers left, and for the IRIS problem 15 hidden centers were selected into the optimal structure of the RBFNN. Moreover, the experimental results also illustrated that the generalization performance of the optimized RBPNN for the two examples was obviously better than the one of the optimized RBFNN.

This paper investigates, from the viewpoint of linear algebra, the local minima of least square error cost functions defined at the outputs of outer-supervised feedforward neural networks (FNN). For a specific case, we also show that those spacedly colinear samples (probably output by the final hidden layer) will be easily separated with null-cost error function even if the condition M/spl ges/N is not satisfied. In the light of these conclusions we shall give a general method for designing a suitable architecture network to solve a specific problem.

Discusses the relationship between the mean square classifier (MSC) and the linear feedforward neural network classifier (LFNNC) and further studies the transformation properties of LFNNs under the supervision of outer-supervised signals. The obtained conclusions show that an LFNNC is equivalent to the cascade of many MSCs, and vice versa (only if the hidden units number is greater than the "bottleneck" limit). Therefore, we can cascade several MSCs to form a modular LFNNC which is completely equivalent to doing information fusion from different MSCs.

This paper proposes a novel Forward–Backward Smoothing-Based Learning Subspace Method (FBSLSM), which can satisfy the equirements of being insensitive to the order of presentation of the training samples, and is of faster convergence speed. This method is applied to the recognition of simulating High Resolution Radar (HRR) targets (two for ships, one for chaff). Moreover, for recognition of HRR targets, a new selection method of subspace dimensionality is given. The computer simulating experiments show that the corresponding performance of proposed FBSLSM such as rate of correct recognition and convergence speed is better than that of the ALSM presented by Oja.

This paper extends general radial basis function networks (RBFN) with Gaussian kernel functions to generalized radial basis function networks (GRBFN) with Parzen window functions, and discusses applying the GRBFNs to recognition of radar targets. The equivalence between the RBFN classifiers (RBFNC) with outer-supervised signals of 0 or 1 and the estimate of Parzen windowed probabilistic density is proved. It is pointed out that the I/O functions of the hidden units in the RBFNC can be extended to general Parzen window functions (or called as potential functions). We present using recursive least square-backpropagation (RLS–BP) learning algorithm to train the GRBFNCs to classify five types of radar targets by means of their one-dimensional cross profiles. The concepts about the rate of recognition and confidence in the process of testing classification performance of the GRBFNCs are introduced. Six generalized kernel functions such as Gaussian, Double-Exponential, Triangle, Hyperbolic, Sinc and Cauchy, are used as the hidden I/O functions of the RBFNCs, and the classification performance of corresponding GRBFNCs for classifying one-dimensional cross profiles of radar targets is discussed.

In this paper, the local minima-free conditions of the outer-supervised feedforward neural networks (FNN) based on batch-style learning are studied by means of the embedded subspace method. It is proven that only if the rendition that the number of the hidden neurons is not less than that of the training samples, which is sufficient but not necessary, is satisfied, the network will necessarily converge to the global minima with null cost, and that the condition that the range space of the outer-supervised signal matrix is included in the range space of the hidden output matrix Is sufficient and necessary condition for the local minima-free in the error surface. In addition, under the condition of the number of the hidden neurons being less than that of the training samples and greater than the number of the output neurons, it is demonstrated that there will also only exist the global minima with null cost in the error surface if the first layer weights are adequately selected.

This paper proposes a united training method of the link weights of the Gaussian radial basis function networks (GRBFN) and the shape parameter α of the RBF. The training method corresponding to the former is a kind of recursive least squares backpropagation (RLS-BP) learning algorithm which is an accurately recursive method, the training method corresponding to the latter is an adaptive gradient descending (AGD) searching algorithm which is an approximately approaching method. We use the one-dimensional images of radar targets to study the effect of the shape parameter α on the rate of recognition, and survey the changes of the shape parameter αs of radial basis functions corresponding to different hidden nodes, and present the judgement confidence curves of different radar targets. In addition, the forgotten factor λ which makes the effects on the speed of convergence is also discussed. The experimental results are presented.

This paper studies the mechanism for classification of feedforward neural networks from the geometric viewpoints. It is pointed out that the multilayer perceptron networks (MLPNs) realize hyperplane divisions in the pattern space, and the FLN realize hypercurved divisions. We give a form of generalized function link nets (GFLN), and discuss the application of a special GFLN to recognition of radar targets, and give several experimental results.