Protein phosphorylation governs cellular signaling, making accurate identification of kinase-specific sites essential for understanding regulatory and disease mechanisms. Although computational approaches have shown promise in inferring kinase specificity, most existing methods primarily rely on local sequence patterns and remain limited in their ability to capture broader contextual information. More critically, experimentally validated kinase-substrate data are inherently scarce and highly imbalanced across kinase groups, substantially restricting the generalization performance of purely discriminative models, especially for underrepresented kinases. To address these challenges, we propose KSDiffusion, a unified framework for kinase-specific phosphorylation site prediction explicitly designed for data-limited and imbalanced regimes. KSDiffusion integrates a protein language model with task-aware conditional diffusion-based generative modeling. Specifically, an ESM-2-based encoder is employed to extract context-aware peptide representations enriched with evolutionary and structural information, while supervised contrastive learning further enhances kinase-specific discriminability in the embedding space. To alleviate data scarcity for rare kinase groups, we introduce a conditional diffusion model, termed KS-DiT, which generates biologically plausible and kinase-consistent synthetic representations that directly support downstream prediction. Comprehensive experiments across kinase groups spanning low-, medium-, and large-data regimes demonstrate that KSDiffusion consistently outperforms representative baseline methods. In particular, substantial improvements are achieved for data-scarce kinase groups, with AUC gains of up to $\sim $15%, while maintaining competitive performance when sufficient training data are available. These results underscore the regime-dependent effectiveness of conditional diffusion-based augmentation and highlight the value of integrating protein language models with task-aware generative modeling for robust kinase-specific phosphorylation site prediction under realistic data constraints.

MOTIVATION: Spatial transcriptomics (ST) enables the measurement of gene expression while preserving the spatial context of tissues. However, the sparsity of ST data leads to poor usage of gene expression and spatial information, resulting in the embeddings that are not well represented and challenging for downstream analyses. RESULTS: Here, we introduced GSG, a generative self-supervised representation learning framework for ST data that leverages a masking mechanism to learn informative representations. For spatial domain identification, GSG consistently outperformed state-of-the-art methods across benchmarking datasets, regardless of sequencing platforms. In addition, we applied GSG to an in-house human fetal heart dataset, revealing anatomically coherent spatial domains and identifying APCDD1 as an endocardial-specific marker potentially involved in congenital heart disease. Our results showcase GSG's superiority and underscore its valuable contributions to advancing ST analysis. AVAILABILITY AND IMPLEMENTATION: Our software package is available at https://github.com/keaml-Guan/GSG.

To address the weight coupling problem, certain studies introduced few-shot Neural Architecture Search (NAS) methods, which partition the supernet into multiple sub-supernets. However, these methods often suffer from computational inefficiency and tend to provide suboptimal partitioning schemes. To address this problem more effectively, we analyze the weight coupling problem from a novel perspective, which primarily stems from distinct modules in succeeding layers imposing conflicting gradient directions on the preceding layer modules. Based on this perspective, we propose the Gradient Contribution (GC) method that efficiently computes the cosine similarity of gradient directions among modules by decomposing the Vector-Jacobian Product during supernet backpropagation. Subsequently, the modules with conflicting gradient directions are allocated to distinct sub-supernets while similar ones are grouped together. To assess the advantages of GC and address the limitations of existing Graph Neural Architecture Search methods, which are limited to searching a single type of Graph Neural Networks (Message Passing Neural Networks (MPNNs) or Graph Transformers (GTs)), we propose the Unified Graph Neural Architecture Search (UGAS) framework, which explores optimal combinations of MPNNs and GTs. The experimental results demonstrate that GC achieves state-of-the-art (SOTA) performance in supernet partitioning quality and time efficiency. In addition, the architectures searched by UGAS+GC outperform both the manually designed GNNs and those obtained by existing NAS methods. Finally, ablation studies further demonstrate the effectiveness of all proposed methods.

Low-light image enhancement (LLIE) aims at restoring the color and brightness of underexposed images. Supervised methods suffer from high costs in collecting paired low-normal light images, while unsupervised approaches require intricate loss functions. To tackle these dual challenges, we propose the Trouble-Maker Learning (TML) strategy, which leverages images with normal light as training inputs. TML comprises two core components. Firstly, the Troublemaker Model (TM) generates pseudo low-light images from normal images, thereby alleviating the need for pairwise data and reducing associated costs. Secondly, the Predicting Model (PM) enhances the brightness of pseudo low-light images. Additionally, we integrate an Enhancing Model (EM) to further refine the visual quality of the PM's outputs. In LLIE tasks, it is crucial to capture global element correlations, as this allows for the extraction of more information pertaining to the same object. Convolutional Neural Networks (CNNs) and self-attention mechanisms are not well-suited to this task due to the local CNN operators, and high time complexity, respectively. To address these limitations, we propose Global Dynamic Convolution (GDC) with a time complexity of O(n). Essentially, GDC mimics the partial calculation process of self-attention to establish element-wise correlations. Building upon the GDC module, we develop the UGDC model. Finally, we explore the application of Data Fusion in the field of LLIE. Based on the Retinex theory, we conducted feature-level fusion using low-light images, illumination components and reflection components, which further enhance the performance of the LLIE system. Extensive quantitative and qualitative experiments demonstrate that UGDC, trained with TML and via data fusion, can achieve performance competitive with state-of-the-art approaches on public datasets. The source code of this paper is publicly available at https://github.com/Rainbowman0/TML_LLIE, facilitating reproducibility of the research findings.

The traveling salesman problem (TSP) is a well-known combinatorial optimization problem (COP) with broad real-world applications. Recently, neural networks (NNs) have gained popularity in this research area because as shown in the literature, they provide strong heuristic solutions to TSPs. Compared to autoregressive neural approaches, nonautoregressive (NAR) networks exploit the inference parallelism to elevate inference speed but suffer from comparatively low solution quality. In this article, we propose a novel NAR model named NAR4TSP, which incorporates a specially designed architecture and an enhanced reinforcement learning (RL) strategy. To the best of our knowledge, NAR4TSP is the first TSP solver that successfully combines RL and NAR networks. The key lies in the incorporation of NAR network output decoding into the training process. NAR4TSP efficiently represents TSP-encoded information as rewards and seamlessly integrates it into RL strategies, while maintaining consistent TSP sequence constraints during both training and testing phases. Experimental results on both synthetic and real-world TSPs demonstrate that NAR4TSP outperforms five state-of-the-art (SOTA) models in terms of solution quality, inference speed, and generalization to unseen scenarios.

Drug-Drug Interaction (DDI) task plays a crucial role in clinical treatment and drug development. Recently, deep learning methods have been successfully applied for DDI prediction. However, training deep learning models always need large amount of data, while known DDIs are scarce. To address this challenge, a graph neural network-based DDI prediction model named PNESR-DDI is proposed, which compensates for the lack of DDIs by enriching drug representations. First, to obtain initial node representations that incorporate rich semantic information from the biomedical knowledge graph (KG), a link prediction pre-training method on external KG is proposed in the node embedding pre-training module. Then, considering the large scale of the KG, subgraph extraction for the target drug pairs is introduced to reduce noise and decrease computational complexity in the subgraph anchoring module. After that, the subgraph is updated, and node similarities are propagated in the subgraph reconstruction module. Based on the node similarity scores, the subgraph is pruned and reconstructed, which adjusts node representations to be more conducive to DDI prediction. Finally, the drug embeddings, subgraph representations, and drug fingerprint features are concatenated to predict DDIs. PNESRDDI is evaluated on two benchmark DDI datasets: DrugBank and TWOSIDES. Experiment results show that PNESR-DDI achieves better performance than baselines. Ablation results validate the effectiveness of the pre-training method and the adaptive subgraph reconstruction strategy.

Identifying drug-target interactions (DTIs) holds significant importance in drug discovery and development, playing a crucial role in various areas such as virtual screening, drug repurposing and identification of potential drug side effects. However, existing methods commonly exploit only a single type of feature from drugs and targets, suffering from miscellaneous challenges such as high sparsity and cold-start problems. We propose a novel framework called MSI-DTI (Multi-Source Information-based Drug-Target Interaction Prediction) to enhance prediction performance, which obtains feature representations from different views by integrating biometric features and knowledge graph representations from multi-source information. Our approach involves constructing a Drug-Target Knowledge Graph (DTKG), obtaining multiple feature representations from diverse information sources for SMILES sequences and amino acid sequences, incorporating network features from DTKG and performing an effective multi-source information fusion. Subsequently, we employ a multi-head self-attention mechanism coupled with residual connections to capture higher-order interaction information between sparse features while preserving lower-order information. Experimental results on DTKG and two benchmark datasets demonstrate that our MSI-DTI outperforms several state-of-the-art DTIs prediction methods, yielding more accurate and robust predictions. The source codes and datasets are publicly accessible at https://github.com/KEAML-JLU/MSI-DTI.

Cartesian Genetic Programming (CGP) has often been applied for Neural Architecture Search (NAS). However, the performance of CGP is less than ideal when searching for architectures with limited computing resources. To better facilitate NAS with limited computing resources, this paper proposes a crossover operator, a light-weighted age mechanism, and two adaptive mutation operators as the novel components in our Efficient Cartesian Genetic Programming (ECGP) method. To assess the performance of ECGP, we conduct extensive experiments on three text classification task datasets. The experimental results demonstrate that ECGP outperforms other NAS methods, requiring only hundreds of fitness evaluations to find architectures with competitive accuracy compared with human-designed models. Additionally, the ECGP-evolved architectures are shown as converging fast and stably, and having high-level transferability with merely a 1-2% accuracy drop. Ablation studies demonstrate the effectiveness of the proposed operators and age mechanism, and identify GRU as the most critical function in the text classification task. Finally, we summarize three design principles observed from the ECGP-evolved architectures that are in line with human-design strategies. To the best of our knowledge, this work introduces the first attention-derived NAS benchmark for the text classification task.

Node classification is an essential problem in graph learning. However, many models typically obtain unsatisfactory performance when applied to few-shot scenarios. Some studies have attempted to combine meta-learning with graph neural networks to solve few-shot node classification on graphs. Despite their promising performance, some limitations remain. First, they employ the node encoding mechanism of homophilic graphs to learn node embeddings, even in heterophilic graphs. Second, existing models based on meta-learning ignore the interference of randomness in the learning process. Third, they are trained using only limited labeled nodes within the specific task, without explicitly utilizing numerous unlabeled nodes. Finally, they treat almost all sampled tasks equally without customizing them for their uniqueness. To address these issues, we propose a novel framework for few-shot node classification called Meta-GPS \(++\) . Specifically, we first adopt an efficient method to learn discriminative node representations on homophilic and heterophilic graphs. Then, we leverage a prototype-based approach to initialize parameters and contrastive learning for regularizing the distribution of node embeddings. Moreover, we apply self-training to extract valuable information from unlabeled nodes. Additionally, we adopt S \({}^{2}\) (scaling and shifting) transformation to learn transferable knowledge from diverse tasks. The results on real-world datasets show the superiority of Meta-GPS \(++\) . Our code is available here .

Heart sounds reflect the function of heart valves and are important for the diagnosis of heart-related diseases. Automated heart sound diagnosis plays an key role in the early detection of cardiovascular diseases. In this paper, a new deep learning-based detection model (2D ViT-1D CRNN) for heart sound signals is designed, which combines one-dimensional time-domain and two-dimensional time-frequency domain features. In this model, a 1D CNN and a BiLSTM are combined into a 1D CRNN module to extract 1D temporal features from the original PCG signals, while 2D time-frequency features are extracted using a 2D ViT module. The classification results are calculated by softmax function after connecting the outputs of the two modules. To validate the effectiveness of the proposed 2D ViT-1D CRNN model, it is applied to two public datasets with different classification tasks. Compared with existing SOTA methods, our proposed method performs best on both datasets, which suggests that the method in this paper can be applied to assist diagnosis of cardiovascular diseases.

Text clustering is a critical step in text data analysis and has been extensively studied by the text mining community. Most existing text clustering algorithms are based on the bag-of-words model, which faces the high-dimensional and sparsity problems and ignores text structural and sequence information. Deep learning-based models such as convolutional neural networks and recurrent neural networks regard texts as sequences but lack supervised signals and explainable results. In this paper, we propose a deep feature-based text clustering (DFTC) framework that incorporates pretrained text encoders into text clustering tasks. This model, which is based on sequence representations, breaks the dependency on supervision. The experimental results show that our model outperforms classic text clustering algorithms on almost all the considered datasets. In addition, the explanation of the clustering results is significant for understanding the principles of the deep learning approach. Our proposed clustering framework includes an explanation module that can help users understand the meaning and quality of the clustering results. Our code is available at https://github.com/KEAML-JLU/DeepTextClustering.

The cold-start problem is a major factor that limits the effectiveness of recommendation systems. Having too few available interaction records brings a series of challenges when predicting user preferences. At present, there are two main kinds of strategies for solving this problem from different perspectives. One is cross-domain recommendation (CDR), which introduces additional information by domain knowledge propagation with transfer learning. However, CDR methods follow traditional training processes in machine learning and cannot solve this typical few-shot problem from the perspective of optimization. The other type of methods that has recently emerged is based on meta-learning. Most of these approaches focus only on generating a meta-model to perform better on new tasks and ignore improvements based on cross-domain information. Therefore, it is necessary to design a novel approach to solve this problem with both domain knowledge and meta-optimization. To achieve this goal, a novel cross-domain meta-learner for cold-start recommendation (MetaCDR) is proposed. In MetaCDR, we design a domain knowledge meta-transfer module to connect different domain networks. In addition, we introduce a pretraining strategy to ensure its efficiency. The experimental results show that MetaCDR performs significantly better than state-of-the-art models in a variety of scenarios.

Automatic heart sound diagnosis plays an important role in the early detection of cardiovascular diseases. Phonocardiogram (PCG) signals are often used in this field f or its low cost and non-invasive advantages. In this paper, we design a new time and time-frequency features integrated CNN (TTFI-CNN) model for heart sound signals detection. In the model, a 1D CNN and a BiLSTM are combined into 1D CRNN module to extract temporal features from the original PCG signal, and a 2D CNN module is applied to capture high-level features from time-frequency domain MFCCs inputs. The outputs of the two modules are recalibrated by attention mechanism to selectively emphasize informative features and suppress less useful ones. To verify the proposed TTFI-CNN model, it is applied to two public datasets with different classification t asks (binary and multiclassification). The TTFI-CNN model has achieved 97.15% accuracy, 97.13% sensitivity, and 97.17% specificity on physionet/cinc database, and obtained 3.31 and 2.61 precision scores on the PASCAL database A and B, respectively. Compared with the previous state-of-the-art methods, the TTFI-CNN performs best on all the above metrics. https://github.com/XxxNnnSssWww/TTFI-CNN.

The subcellular localization of proteins is very important for further understanding their functions. This paper proposes a deep learning method called PBLoc to predict the protein subcellular localization from sequence information only. In the PBLoc method, the pre-trained protein model–ProtBert, is utilized to extract the features of the protein sequence, followed by a bidirectional GRU model to predict subcellular localization. In order to capture sequence features more accurately, an attention mechanism is also used in the model. To verify the performance of our proposed PBLoc method, it is applied to DeepLoc benchmark dataset. For comparison, other 8 SOTA algorithms are also executed. The results show that PBLoc without using any evolution feature outperforms the other compared algorithms.

Coronavirus disease 2019 (COVID-19) has infected hundreds of millions of people and killed millions of them. As an RNA virus, COVID-19 is more susceptible to variation than other viruses. Many problems involved in this epidemic have made biosafety and biosecurity (hereafter collectively referred to as 'biosafety') a popular and timely topic globally. Biosafety research covers a broad and diverse range of topics, and it is important to quickly identify hotspots and trends in biosafety research through big data analysis. However, the data-driven literature on biosafety research discovery is quite scant. We developed a novel topic model based on latent Dirichlet allocation, affinity propagation clustering and the PageRank algorithm (LDAPR) to extract knowledge from biosafety research publications from 2011 to 2020. Then, we conducted hotspot and trend analysis with LDAPR and carried out further studies, including annual hot topic extraction, a 10-year keyword evolution trend analysis, topic map construction, hot region discovery and fine-grained correlation analysis of interdisciplinary research topic trends. These analyses revealed valuable information that can guide epidemic prevention work: (1) the research enthusiasm over a certain infectious disease not only is related to its epidemic characteristics but also is affected by the progress of research on other diseases, and (2) infectious diseases are not only strongly related to their corresponding microorganisms but also potentially related to other specific microorganisms. The detailed experimental results and our code are available at https://github.com/KEAML-JLU/Biosafety-analysis.

Text clustering is a critical step in text data analysis and has been extensively studied by the text mining community. Most existing text clustering algorithms are based on the bag-of-words model, which faces the high-dimensional and sparsity problems and ignores text structural and sequence information. Deep learning-based models such as convolutional neural networks and recurrent neural networks regard texts as sequences but lack supervised signals and explainable results. In this paper, we propose adeepfeature-basedtextclustering (DFTC) framework that incorporates pretrained text encoders into text clustering tasks. This model, which is based on sequence representations, breaks the dependency on supervision. The experimental results show that our model outperforms classic text clustering algorithms and the state-of-the-art pretrained language model, i.e., BERT, on almost all the considered datasets. In addition, the explanation of the clustering results is significant for understanding the principles of the deep learning approach. Our proposed clustering framework includes an explanation module that can help users understand the meaning and quality of the clustering results.

The Traditional Chinese Medicine’s ancient literature recorded the massive medical theories and abundant medical experiences. To better understand and utilize, the knowledge from the literature, the Acupuncture and Tuina Knowledge Graph is proposed in this paper. Meanwhile, a deep learning network is established for acupuncture and tuina-related entity recognition and entity-relationship extraction. Finally, the trained network is able to reach an 82%+ F1-score for NER and 70%+ F1-score for relationship extraction.

The worldwide spread of COVID-19 has made a severe impact on human health and life. It has shown rapid propagation, long in vitro survival, and a long incubation period. More seriously, COVID-19 is more susceptible to variation, as it is an RNA virus. Mutations of COVID-19 have been reported in multiple countries worldwide, which makes drug and vaccine development a significant challenge. To search for potential drugs and vaccines and reveal the atlas of COVID-19 evolution, we extract information from massive unstructured data and construct a COVID-19 knowledge graph using the COVID-19 data. Based on machine learning approaches, we infer and predict novel coronavirus pneumonia-related diseases, drug action targets, etc. to speculate on new and more effective treatment methods. In addition, to study transcriptome of SARS-CoV-2, new ideas can be provided to biomedical experts with flexible responses to viral variation. An in-depth analysis of the COVID-19 pathomechanism at the pharmaceutical, genetic, and protein levels provides effective means and tools for novel coronavirus pneumonia vaccines, drug development, and therapeutic program design.

Text classification is a fundamental task with broad applications in natural language processing.Recently, graph neural networks (GNNs) have attracted much attention due to their powerful representation ability.However, most existing methods for text classification based on GNNs consider only one-hop neighborhoods and low-frequency information within texts, which cannot fully utilize the rich context information of documents.Moreover, these models suffer from over-smoothing issues if many graph layers are stacked.In this paper, a Deep Attention Diffusion Graph Neural Network (DADGNN) model is proposed to learn text representations, bridging the chasm of interaction difficulties between a word and its distant neighbors.Experimental results on various standard benchmark datasets demonstrate the superior performance of the present approach.

Circular RNAs (circRNAs) are a unique class of RNA molecule identified more than 40 years ago which are produced by a covalent linkage via back-splicing of linear RNA. Recent advances in sequencing technologies and bioinformatics tools have led directly to an ever-expanding field of types and biological functions of circRNAs. In parallel with technological developments, practical applications of circRNAs have arisen including their utilization as biomarkers of human disease. Currently, circRNA-associated bioinformatics tools can support projects including circRNA annotation, circRNA identification and network analysis of competing endogenous RNA (ceRNA). In this review, we collected about 100 circRNA-associated bioinformatics tools and summarized their current attributes and capabilities. We also performed network analysis and text mining on circRNA tool publications in order to reveal trends in their ongoing development.

MOTIVATION: As large amounts of biological data continue to be rapidly generated, a major focus of bioinformatics research has been aimed toward integrating these data to identify active pathways or modules under certain experimental conditions or phenotypes. Although biologically significant modules can often be detected globally by many existing methods, it is often hard to interpret or make use of the results toward pathway model generation and testing. RESULTS: To address this gap, we have developed the IMPRes algorithm, a new step-wise active pathway detection method using a dynamic programing approach. IMPRes takes advantage of the existing pathway interaction knowledge in Kyoto Encyclopedia of Genes and Genomes. Omics data are then used to assign penalties to genes, interactions and pathways. Finally, starting from one or multiple seed genes, a shortest path algorithm is applied to detect downstream pathways that best explain the gene expression data. Since dynamic programing enables the detection one step at a time, it is easy for researchers to trace the pathways, which may lead to more accurate drug design and more effective treatment strategies. The evaluation experiments conducted on three yeast datasets have shown that IMPRes can achieve competitive or better performance than other state-of-the-art methods. Furthermore, a case study on human lung cancer dataset was performed and we provided several insights on genes and mechanisms involved in lung cancer, which had not been discovered before. AVAILABILITY AND IMPLEMENTATION: IMPRes visualization tool is available via web server at http://digbio.missouri.edu/impres. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The surprisingly popular decision in social science fields is a wisdom of the crowd technique that taps into the expert minority opinion within a crowd, which has been demonstrated to be remarkably effective for multiple questions. Most of the existing PSO variants construct the exemplars by solely using fitness, which could be viewed as the democratic approaches or methods. However, the democratic methods tend to highlight the most popular opinion, not necessarily the most correct, which might lead the population into a local trapping region in the scenarios of swarm intelligent computing and evolutionary computation. This paper proposes a method to implement the surprisingly popular decision in PSO to facilitate the exemplar construction, cooperating with the dynamic topology maintenance. The proposed PSO variant is called the Surprisingly Popular Algorithm-based Comprehensive Adaptive Topology Learning Particle Swarm Optimization (SPA-CatlePSO). By using the dynamic topological connection and surprisingly popular decision strategy, the proposed SPA-CatlePSO could adjust the degree of small world topology, mimicking the mechanism of knowledge conversion in the crowd, and guide the direction of the exploitation by constructing exemplars with the largest surprisingly popular degree. We evaluate the proposed SPA-CatlePSO on the full CEC2014 benchmark suite and compare its validity with OLPSO, TSLPSO, ASDPSO, HCLPSO, OptBees and L-shade. The experimental results show that the SPA-CatlePSO algorithm is competitive with the most advanced swarm-based intelligent algorithms.

Discovering new long non-coding RNAs (lncRNAs) has been a fundamental step in lncRNA-related research. Nowadays, many machine learning-based tools have been developed for lncRNA identification. However, many methods predict lncRNAs using sequence-derived features alone, which tend to display unstable performances on different species. Moreover, the majority of tools cannot be re-trained or tailored by users and neither can the features be customized or integrated to meet researchers' requirements. In this study, features extracted from sequence-intrinsic composition, secondary structure and physicochemical property are comprehensively reviewed and evaluated. An integrated platform named LncFinder is also developed to enhance the performance and promote the research of lncRNA identification. LncFinder includes a novel lncRNA predictor using the heterologous features we designed. Experimental results show that our method outperforms several state-of-the-art tools on multiple species with more robust and satisfactory results. Researchers can additionally employ LncFinder to extract various classic features, build classifier with numerous machine learning algorithms and evaluate classifier performance effectively and efficiently. LncFinder can reveal the properties of lncRNA and mRNA from various perspectives and further inspire lncRNA-protein interaction prediction and lncRNA evolution analysis. It is anticipated that LncFinder can significantly facilitate lncRNA-related research, especially for the poorly explored species. LncFinder is released as R package (https://CRAN.R-project.org/package=LncFinder). A web server (http://bmbl.sdstate.edu/lncfinder/) is also developed to maximize its availability.

MOTIVATION: Computational methods for protein post-translational modification (PTM) site prediction provide a useful approach for studying protein functions. The prediction accuracy of the existing methods has significant room for improvement. A recent deep-learning architecture, Capsule Network (CapsNet), which can characterize the internal hierarchical representation of input data, presents a great opportunity to solve this problem, especially using small training data. RESULTS: We proposed a CapsNet for predicting protein PTM sites, including phosphorylation, N-linked glycosylation, N6-acetyllysine, methyl-arginine, S-palmitoyl-cysteine, pyrrolidone-carboxylic-acid and SUMOylation sites. The CapsNet outperformed the baseline convolutional neural network architecture MusiteDeep and other well-known tools in most cases and provided promising results for practical use, especially in learning from small training data. The capsule length also gives an accurate estimate for the confidence of the PTM prediction. We further demonstrated that the internal capsule features could be trained as a motif detector of phosphorylation sites when no kinase-specific phosphorylation labels were provided. In addition, CapsNet generates robust representations that have strong discriminant power in distinguishing kinase substrates from different kinase families. Our study sheds some light on the recognition mechanism of PTMs and applications of CapsNet on other bioinformatic problems. AVAILABILITY AND IMPLEMENTATION: The codes are free to download from https://github.com/duolinwang/CapsNet_PTM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Large-scale knowledge graphs have currently reached impressive sizes; however, they are still far from complete. In addition, most existing methods for knowledge graph completion only consider the direct links between entities, ignoring the vital impact of the semantics of relation paths. In this paper, we study the problem of how to better embed entities and relations of knowledge graphs into different low-dimensional spaces by taking full advantage of the additional semantics of relation paths and propose a novel relation path embedding model named as RPE. Specifically, with the corresponding relation and path projections, RPE can simultaneously embed each entity into two types of latent spaces. Moreover, type constraints are extended from traditional relation-specific type constraints to the proposed path-specific type constraints and both of the two type constraints can be seamlessly incorporated into RPE. The proposed model is evaluated on the benchmark tasks of link prediction and triple classification. The results of experiments demonstrate our method outperforms all baselines on both tasks. They indicate that our model is capable of catching the semantics of relation paths, which is significant for knowledge representation learning.

Automatically describing contents of an image using natural language has drawn much attention because it not only integrates computer vision and natural language processing but also has practical applications. Using an end-to-end approach, we propose a bidirectional semantic attention-based guiding of long short-term memory (Bag-LSTM) model for image captioning. The proposed model consciously refines image features from previously generated text. By fine-tuning the parameters of convolution neural networks, Bag-LSTM obtains more text-related image features via feedback propagation than other models. As opposed to existing guidance-LSTM methods which directly add image features into each unit of an LSTM block, our fine-tuned model dynamically leverages more text-conditional image features, acquired by the semantic attention mechanism, as guidance information. Moreover, we exploit bidirectional gLSTM as the caption generator, which is capable of learning long term relations between visual features and semantic information by making use of both historical and future contextual information. In addition, variations of the Bag-LSTM model are proposed in an effort to sufficiently describe high-level visual-language interactions. Experiments on the Flickr8k and MSCOCO benchmark datasets demonstrate the effectiveness of the model, as compared with the baseline algorithms, such as it is 51.2% higher than BRNN on CIDEr metric.

Gastric cancer is one of the top leading causes of cancer mortality worldwide especially in China. In recent years, some lncRNAs are discovered to be dysregulated in many cancers. The study on long non-coding RNAs (lncRNAs) relationship with cancers has attracted increasing attention. The molecular mechanism of gastric cancer remains largely unclear factors, especially for lncRNAs. Experiments are feasible to obtain related information, however, experimental identification of cancer-related lncRNAs usually possesses high time complexity and high cost. In this paper, a computational method is proposed to determine the relationship between lncRNA and gastric cancer by reusing the exon-based array of gastric cancer. One specific lncRNAs LINC00365 and its target differentially expressed genes whose products are predicted as blood, urine, or salvia-excretory are identified to be candidates for a combined biomarker for gastric cancer. Further biological function and molecular mechanism of the gastric cancer related lncRNAs and coding gene biomarkers are inferred in terms of multi-source biological knowledge.

As computer science and information technology are making broad and deep impacts on our daily lives, more and more papers are being submitted to computer science journals and conferences. To help authors decide where they should submit their manuscripts, we present the Content-based Journals & Conferences Recommender System on computer science, as well as its web service at http://www.keaml.cn/prs/ . This system recommends suitable journals or conferences with a priority order based on the abstract of a manuscript. To follow the fast development of computer science and technology, a web crawler is employed to continuously update the training set and the learning model. To achieve interactive online response, we propose an efficient hybrid model based on chi-square feature selection and softmax regression. Our test results show that, the system can achieve an accuracy of 61.37% and suggest the best journals or conferences in about 5 s on average.

Diabetes mellitus and obesity are becoming some of the most serious public health challenges in the world. To help researchers more quickly reveal the complex relationships existing between diabetes mellitus, obesity, and related diseases in the literature, and give them an inspiration to search the effective treatments for these diseases, we propose a novel model named as representative latent Dirichlet allocation topic model (RLDA). We conducted the representation learning model on more than 337,000 pieces of diabetes and obesity related literature published in the recent decade. Then, an explicit analysis of the final result using a series of visualization tools to discover meaningful relations among diabetes mellitus, obesity, and other diseases was performed. In order to show the credibility of our discoveries, we used clinical reports, such as Standards of Medical Care in Diabetes, which were not used in our training data, to verify our results. Fortunately, a sufficient number of the reports were direct matches. With the help of our model, we achieved satisfactory results for diabetes mellitus and obesity. For example, we discovered that 22 other diseases are closely related to diabetes mellitus, 10 with obesity and 8 with both. In addition, the tumor, adolescent/child, inflammation, and hypertension will be the hottest research topics relating to diabetes and obesity in the near future. We believe that the representational learning model we have built can help biomedical researchers direct the focus and adjust the direction of their work.

A central goal of systems biology is to uncover the underlying functional architecture of the cell and study its mechanisms. To this end, large amounts of omics data are being rapidly generated, and a focus of bioinformatics research has been towards integrating these data to identify active pathways or modules under certain conditions. Many bioinformatics algorithms include optimization methods, statistical methods, and methods using interaction network topology attributes have been applied for this. Although biologically significant modules can often be detected globally by these methods, it is hard to interpret or make use of the results towards in silico hypothesis generation and testing. We propose a step-wise active pathway detection method (IMPRes) using a dynamic programming approach. First, we take advantage of the existing pathway interaction knowledge in KEGG to build a background network, and then starting from one or multiple receptors of a certain perturbation, we use transcriptomics data collected under these conditions to detect paths that best explain the variations of genes downstream. More other omics data will be integrated in the future. Since dynamic programming enables the detection one step a time, it is easy for biomedical researchers to trace the pathway and finally lead to more accurate drug design and more effective treatment strategies. Additionally, by adding protein-protein interactions in our method, the hypotheses that we generate do not merely utilize existing knowledge, but have potential to discover new knowledge. We have evaluated our method on a dataset of cell wall stress in yeast. The path we found highly agrees with the Cell Wall Integrity (CWI) pathway, which is the main signaling pathway involved in the regulation of cell wall stress responses. We have also compared with other methods on a yeast high osmolality stress dataset and achieved an overall better performance than some other methods. More experiments have been done on human cancer datasets and mouse datasets. Finally, the IMPRes web server is established to offer a simple interface for applying IMPRes. Users can upload their own data and obtain an interactive visualization of the resulting pathway map. Users can further filter or highlight interactions according to pathway information or relation types. All genes in the pathway map are listed with detailed annotations. The IMPRes web server is available at http://gene.rnet.missouri.edu/soykb_dev/IMPRes/.

MOTIVATION: Computational methods for phosphorylation site prediction play important roles in protein function studies and experimental design. Most existing methods are based on feature extraction, which may result in incomplete or biased features. Deep learning as the cutting-edge machine learning method has the ability to automatically discover complex representations of phosphorylation patterns from the raw sequences, and hence it provides a powerful tool for improvement of phosphorylation site prediction. RESULTS: We present MusiteDeep, the first deep-learning framework for predicting general and kinase-specific phosphorylation sites. MusiteDeep takes raw sequence data as input and uses convolutional neural networks with a novel two-dimensional attention mechanism. It achieves over a 50% relative improvement in the area under the precision-recall curve in general phosphorylation site prediction and obtains competitive results in kinase-specific prediction compared to other well-known tools on the benchmark data. AVAILABILITY AND IMPLEMENTATION: MusiteDeep is provided as an open-source tool available at https://github.com/duolinwang/MusiteDeep. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: RNAs have been found to carry diverse functionalities in nature. Inferring the similarity between two given RNAs is a fundamental step to understand and interpret their functional relationship. The majority of functional RNAs show conserved secondary structures, rather than sequence conservation. Those algorithms relying on sequence-based features usually have limitations in their prediction performance. Hence, integrating RNA structure features is very critical for RNA analysis. Existing algorithms mainly fall into two categories: alignment-based and alignment-free. The alignment-free algorithms of RNA comparison usually have lower time complexity than alignment-based algorithms. RESULTS: An alignment-free RNA comparison algorithm was proposed, in which novel numerical representations RNA-TVcurve (triple vector curve representation) of RNA sequence and corresponding secondary structure features are provided. Then a multi-scale similarity score of two given RNAs was designed based on wavelet decomposition of their numerical representation. In support of RNA mutation and phylogenetic analysis, a web server (RNA-TVcurve) was designed based on this alignment-free RNA comparison algorithm. It provides three functional modules: 1) visualization of numerical representation of RNA secondary structure; 2) detection of single-point mutation based on secondary structure; and 3) comparison of pairwise and multiple RNA secondary structures. The inputs of the web server require RNA primary sequences, while corresponding secondary structures are optional. For the primary sequences alone, the web server can compute the secondary structures using free energy minimization algorithm in terms of RNAfold tool from Vienna RNA package. CONCLUSION: RNA-TVcurve is the first integrated web server, based on an alignment-free method, to deliver a suite of RNA analysis functions, including visualization, mutation analysis and multiple RNAs structure comparison. The comparison results with two popular RNA comparison tools, RNApdist and RNAdistance, showcased that RNA-TVcurve can efficiently capture subtle relationships among RNAs for mutation detection and non-coding RNA classification. All the relevant results were shown in an intuitive graphical manner, and can be freely downloaded from this server. RNA-TVcurve, along with test examples and detailed documents, are available at: http://ml.jlu.edu.cn/tvcurve/ .

Particle swarm optimizations (PSOs) are drawing extensive attention from both research and engineering fields due to their simplicity and powerful global search ability. However, there are two issues needing to be improved: one is that the classical PSO converges slowly; the other is that classical PSO tends to result in premature convergence, especially for multi-modal problems. This paper attempts to address these two issues. Firstly, to improve the convergent efficiency, this paper proposes an asymptotic predicting model of the globally-optimal solution, which is used to predict the global optimum based on extracting the features reflecting the evolutionary trend. The predicted global optimum is then taken as the third exemplar, in a way similar to the individual historical best solution and the swarm historical best solution in guiding the evolutionary process of other particles. To reduce the probability that the population is trapped into a local optimum due to the premature phenomenon, this paper proposes an adaptive mutation strategy, which is used to help the trapped particles to escape away from the local optimum by using the extended non-uniform mutation operator. Finally, we combine the two entities to develop a globally-optimal prediction-based adaptive mutation particle swarm optimization (GPAM-PSO). In numerical experimental parts, we compare the proposed GPAM-PSO with 11 existing PSO variants by using 22 benchmark problems of 30-dimensions and 100-dimensions, respectively. Numerical experiments demonstrate that the proposed GPAM-PSO could improve the accuracy and efficiency remarkably, which means that the combination of the globally-optimal prediction-based search and the adaptive mutation strategy could accelerate the convergence and reduce premature phenomenon effectively. Generally speaking, GPAM-PSO performs most efficiently and robustly. Moreover, the performance on an engineering problem demonstrates the practical application of the proposed GPAM-PSO algorithm.

Essential proteins are crucial to cellular survival and development. Traditionally, essential proteins are identified by knock-out experiments, which are expensive and often fatal to the target organisms. Regarding this, an important approach to essential protein identification is through computational prediction. In this research, we present a novel computational method, Integrated Edge Weights (IEW), to innovatively predict proteins' essentiality based on essential protein-protein interactions. The experimental results on all three organisms: Saccharomyces cere-visiae (Yeast), Escherichia coli (E. coli), and Caenorhabditis ele-gans (C. elegans) show that IEW achieves better performance than the state-of-the-art methods in terms of precision-recall. Furthermore, we have demonstrated that the highly-ranked protein-protein interactions predicted by our approach tend to be biologically significant in Yeast, E. coli, and C. elegans protein-protein interaction (PPI) networks.

Simple Tabular Reduction algorithms (STR) work well to establish Generalized Arc Consistency (GAC) on positive table constraints. However, the existing STR algorithms are useless for negative table constraints. In this work, we propose a novel STR algorithm and its improvement, which work on negative table constraints. Our preliminary experiments are performed on some random instances and a certain benchmark instances. The results show that the new algorithms outperform GAC-valid and the MDD-based GAC algorithm.

A huge amount of microarray datasets are produced with big number of genes and small samples. Feature selection methods have become a very sharp tool to select the gene signatures from the whole gene set. In recent years, researchers are concerned much about the datasets containing samples of cancer as well as corresponding control tissues. However, few feature selection methods consider the effect of paired samples. In this article, we propose a new feature selection method for paired microarray datasets based on the original paired t-test approach. We apply on the paired datasets across six common cancer types. Through comparison with some widely used methods on the performance of prediction power, stability of gene lists and functional stability, our method shows excellent performance. The proposed method has good effectiveness, stability and consistency, which enables the method to be applicative to feature selection for paired microarray expression data analysis.

BACKGROUND: MicroRNAs (miRNAs) are identified in nearly all plants where they play important roles in development and stress responses by target mRNA cleavage or translation repression. MiRNAs exert their functions by sequence complementation with target genes and hence their targets can be predicted using bioinformatics algorithms. In the past two decades, microarray technology has been employed to study genes involved in important biological processes such as biotic response, abiotic response, and specific tissues and developmental stages, many of which are miRNA targets. Despite their value in assisting research work for plant biologists, miRNA target genes are difficult to access without pre-processing and assistance of necessary analytical and visualization tools because they are embedded in a large body of microarray data that are scattered around in public databases. DESCRIPTION: Plant MiRNA Target Expression Database (PMTED) is designed to retrieve and analyze expression profiles of miRNA targets represented in the plethora of existing microarray data that are manually curated. It provides a Basic Information query function for miRNAs and their target sequences, gene ontology, and differential expression profiles. It also provides searching and browsing functions for a global Meta-network among species, bioprocesses, conditions, and miRNAs, meta-terms curated from well annotated microarray experiments. Networks are displayed through a Cytoscape Web-based graphical interface. In addition to conserved miRNAs, PMTED provides a target prediction portal for user-defined novel miRNAs and corresponding target expression profile retrieval. Hypotheses that are suggested by miRNA-target networks should provide starting points for further experimental validation. CONCLUSIONS: PMTED exploits value-added microarray data to study the contextual significance of miRNA target genes and should assist functional investigation for both miRNAs and their targets. PMTED will be updated over time and is freely available for non-commercial use at http://pmted.agrinome.org.

Clustering is used for land-cover identification in remote sensing images when training data are not available. However, in many applications, it is often possible to collect a small number of labeled samples. To effectively exploit this small number of labeled samples combined with a multitude of the unlabeled data, we present a novel semisupervised clustering technique [incremental and decremental affinity propagation (ID-AP)] that incorporates labeled exemplars into the AP algorithm. Unlike standard semisupervised clustering methods, the proposed technique improves the performance by using both the labeled samples to adjust the similarity matrix and an ID-learning principle for unlabeled data selection and useless labeled samples rejection, respectively. This avoids both learning-bias and stability-plasticity dilemma. In order to assess the effectiveness of the proposed ID-AP technique, the experimental analysis was carried out on three different kinds of multispectral images including different percentages of labeled samples. In the analysis, we also studied the accuracy and the stability of two semisupervised clustering algorithms [i.e., constrainedk-means and semisupervised AP (SAP)] and one incremental semisupervised clustering algorithm (i.e., incremental SAP). Experimental results demonstrate that the proposed ID-AP technique adequately captures and takes full advantage of the intrinsic relationship between the labeled samples and unlabeled data, and produces better performance than the other considered methods.

BACKGROUND: In recent years, the important functional roles of RNAs in biological processes have been repeatedly demonstrated. Computing the similarity between two RNAs contributes to better understanding the functional relationship between them. But due to the long-range correlations of RNA, many efficient methods of detecting protein similarity do not work well. In order to comprehensively understand the RNA's function, the better similarity measure among RNAs should be designed to consider their structure features (base pairs). Current methods for RNA comparison could be generally classified into alignment-based and alignment-free. RESULTS: In this paper, we propose a novel wavelet-based method based on RNA triple vector curve representation, named multi-scale RNA comparison. Firstly, we designed a novel numerical representation of RNA secondary structure termed as RNA triple vectors curve (TV-Curve). Secondly, we constructed a new similarity metric based on the wavelet decomposition of the TV-Curve of RNA. Finally we also applied our algorithm to the classification of non-coding RNA and RNA mutation analysis. Furthermore, we compared the results to the two well-known RNA comparison tools: RNAdistance and RNApdist. The results in this paper show the potentials of our method in RNA classification and RNA mutation analysis. CONCLUSION: We provide a better visualization and analysis tool named TV-Curve of RNA, especially for long RNA, which can characterize both sequence and structure features. Additionally, based on TV-Curve representation of RNAs, a multi-scale similarity measure for RNA comparison is proposed, which can capture the local and global difference between the information of sequence and structure of RNAs. Compared with the well-known RNA comparison approaches, the proposed method is validated to be outstanding and effective in terms of non-coding RNA classification and RNA mutation analysis. From the numerical experiments, our proposed method can capture more efficient and subtle relationship of RNAs.

Based on an effective clustering algorithm-Affinity Propagation (AP)-we present in this paper a novel semisupervised text clustering algorithm, called Seeds Affinity Propagation (SAP). There are two main contributions in our approach: 1) a new similarity metric that captures the structural information of texts, and 2) a novel seed construction method to improve the semisupervised clustering process. To study the performance of the new algorithm, we applied it to the benchmark data set Reuters-21578 and compared it to two state-of-the-art clustering algorithms, namely, k-means algorithm and the original AP algorithm. Furthermore, we have analyzed the individual impact of the two proposed contributions. Results show that the proposed similarity metric is more effective in text clustering (F-measures ca. 21 percent higher than in the AP algorithm) and the proposed semisupervised strategy achieves both better clustering results and faster convergence (using only 76 percent iterations of the original AP). The complete SAP algorithm obtains higher F-measure (ca. 40 percent improvement over k-means and AP) and lower entropy (ca. 28 percent decrease over k-means and AP), improves significantly clustering execution time (20 times faster) in respect that k-means, and provides enhanced robustness compared with all other methods.

Due to a high number of spectral channels and a large information quantity, multispectral remote-sensing images are difficult to be classified with high accuracy and efficiency by conventional classification methods, particularly when training data are not available and when unsupervised clustering techniques should be considered for data analysis. In this paper, we propose a novel image clustering method [called fuzzy-statistics-based affinity propagation (FS-AP)] which is based on a fuzzy statistical similarity measure (FSS) to extract land-cover information in multispectral imagery. AP is a clustering algorithm proposed recently in the literature, which exhibits a fast execution speed and finds clusters with small error, particularly for large datasets. FSS can get objective estimates of how closely two pixel vectors resemble each other. The proposed method simultaneously considers all data points to be equally suitable as initial exemplars, thus reducing the dependence of the final clustering from the initialization. Results obtained on three kinds of multispectral images (Landsat-7 ETM+, Quickbird, and moderate resolution imaging spectroradiometer) by comparing the proposed technique with K-means, fuzzy K-means, and AP based on Euclidean distance (ED-AP) demonstrate the good efficiency and high accuracy of FS-AP.

Affinity propagation is an impressive clustering algorithm which was published in Science, 2007. However, the original algorithm couldn't cope with part known data directly. Focusing on this issue, a semi-supervised scheme called incremental affinity propagation clustering is proposed in the paper. In the scheme, the pre-known information is represented by adjusting similarity matrix. Moreover, an incremental study is applied to amplify the prior knowledge. To examine the effectiveness of the method, we concentrate it to text clustering problem and describe the specific method accordingly. The method is applied to the benchmark data set Reuters-21578. Numerical results show that the proposed method performs very well on the data set and has most advantages over two other commonly used clustering methods.

This paper studies quantitatively a high dimensional delayed neural network with small world connection. On the basis of Lyapunov stability approach, we investigate the asymptotic stability of the trivial equilibrium and obtain delay-dependent criteria ensuring global stability for the neural network. It shows that the small world connection decreases the global stability interval. Special attention is paid to the complex dynamics due to the short-cut connenction. Some complex dynamical behaviors are exhibited numerically such as period-doubling bifurcation and quasi-period bifurcation to chaos. It would be promising that small world connection can be used as an effective scheme to control the dynamics.

MOTIVATION: Mislabeled samples often appear in gene expression profile because of the similarity of different sub-type of disease and the subjective misdiagnosis. The mislabeled samples deteriorate supervised learning procedures. The LOOE-sensitivity algorithm is an approach for mislabeled sample detection for microarray based on data perturbation. However, the failure of measuring the perturbing effect makes the LOOE-sensitivity algorithm a poor performance. The purpose of this article is to design a novel detection method for mislabeled samples of microarray, which could take advantage of the measuring effect of data perturbations. RESULTS: To measure the effect of data perturbation, we define an index named perturbing influence value (PIV), based on the support vector machine (SVM) regression model. The Column Algorithm (CAPIV), Row Algorithm (RAPIV) and progressive Row Algorithm (PRAPIV) based on the PIV value are proposed to detect the mislabeled samples. Experimental results obtained by using six artificial datasets and five microarray datasets demonstrate that all proposed methods in this article are superior to LOOE-sensitivity. Moreover, compared with the simple SVM and CL-stability, the PRAPIV algorithm shows an increase in precision and high recall. AVAILABILITY: The program and source code (in JAVA) are publicly available at http://ccst.jlu.edu.cn/CSBG/PIVS/index.htm

A novel time delay neural network is proposed for dynamical system control. In this work, A continuous recurrent neural network with time delay neurons in hidden layer is constructed, and the novel training algorithm and control law independent of delay are developed based on Lyapunovpsilas stability approach. Using the proposed method, the control error converges to a range near the zero point and remains within the domain throughout the course of the execution. The usefulness and validity of the presented algorithm are examined by numerical experiments.

Principal component analysis (PCA) is a favorite multivariate statistical method for image enhancement and compression. However, it is well known that the classical PCA is sensitive to outliers and missing data. Fortunately, fuzzy statistics is an effective theory for processing these kinds of data. Fuzziness and randomicity are just the important characteristics of the data of remote-sensing images. Therefore, by introducing fuzzy statistics variables into classical PCA methods, a novel method for multispectral image processing called fuzzy-statistics-based PCA (FS-PCA) is proposed in this paper. To verify our proposed method, both the classical PCA and the FS-PCA are applied to the multispectral Landsat ETM+ data for image enhancement. The experimental results show that the differences among surface characteristics are expanded sufficiently and that the accuracy of surface feature recognition is improved greatly.

The optimization of job-shop scheduling is very important because of its theoretical and practical significance. In this paper, a computationally effective algorithm of combining PSO with AIS for solving the minimum makespan problem of job-shop scheduling is proposed. In the particle swarm system, a novel concept for the distance and velocity of a particle is presented to pave the way for the job-shop scheduling problem. In the artificial immune system, the models of vaccination and receptor editing are designed to improve the immune performance. The proposed algorithm effectively exploits the capabilities of distributed and parallel computing of swarm intelligence approaches. The algorithm is examined by using a set of benchmark instances with various sizes and levels of hardness and is compared with other approaches reported in some existing literature works. The computational results validate the effectiveness of the proposed approach.

In this paper, we introduce two improvements on ant colony optimization (ACO) algorithm: route optimization and individual variation. The first is an optimized implementation of ACO, by which the running time of ants routing is largely reduced. The results of the simulated experiments show that the improved algorithm not only reduces the number of routing in the ACO but also surpasses existing algorithms in performance in solving large-scale TSP problems. In the second improvement, we introduce individual variation to ACO, by which the ants have different routing strategies. Simulation results show that the speed of convergence of ACO algorithm could be enhanced greatly.

Inspired by the idea of genetic algorithm, we propose two hybrid evolutionary algorithms based on PSO and GA methods through crossing over the PSO and GA algorithms. The main ideas of the two proposed methods are to integrate PSO and GA methods in parallel and series forms respectively. Simulations for a series of benchmark test functions show that both of the two proposed methods possess better ability to find the global optimum than that of the standard PSO algorithm.

An algorithm for solving multiobjective optimization problems is presented based on PSO through the improvement of the selection manner for global and individual extremum. The search for the Pareto optimal set of multiobjective optimization problems is performed. Numerical simulations show the effectiveness of the proposed algorithm.