Splice-altering variants (SAVs) are the second most prevalent class of pathogenic genetic variants and are strongly associated with the occurrence and development of various diseases. However, current computational tools exhibit limited predictive capability beyond canonical GT-AG splice sites, making accurate assessment of noncanonical SAV pathogenicity a considerable challenge. To address this limitation, we developed MOSAIC (multimodal feature fusion for noncanonical splice-altering variants pathogenicity prediction), a deep learning framework designed for precise assessment of noncanonical SAV pathogenicity. MOSAIC integrates long-range contextual signals derived from a pretrained DNA language model, local sequence features captured from multi-scale convolutional neural networks, and functional annotations. By employing a transformer encoder and a gated fusion module, the model adaptively integrates these multimodal features. Benchmarking across multiple independent datasets demonstrated that MOSAIC consistently outperforms existing state-of-the-art methods, such as CADD and SpliceAI. It remains highly accurate and robust when evaluated on rare variants, gene-independent contexts, and the largest subset where all comparative methods yielded outputs. Furthermore, feature importance analysis revealed that long-range dependencies in DNA sequences and transformer-based integration were critical contributors to model performance. Interpretability analyses indicated that MOSAIC could identify key regulatory sequence motifs associated with transcription factors and RNA-binding proteins, offering mechanistic insight into how noncanonical SAVs disrupt splicing regulation and contribute to pathogenic processes. Overall, MOSAIC offers an accurate and interpretable framework for predicting the pathogenicity of noncanonical SAVs, thereby serving as a dependable computational tool for genetic diagnostics and precision medicine applications. MOSAIC source code and data are available at https://github.com/Lilab-genomics/MOSAIC.

The development of single-cell RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for elucidating cell heterogeneity and gene expression. Identifying and discovering cell types through cell clustering is a crucial step in analyzing scRNA-seq data. However, the high-dimensionality nature and frequent dropout events of the data raise great challenges for cell clustering. Here, we propose a novel contrastive clustering framework called scSCCNIA (Similarity-matrix-based Contrastive Clustering with Neighbor Information Aggregation), for the accurate identification of cell clusters from scRNA-seq data. scSCCNIA adopts a Laplacian filter to conduct neighbor information aggregation, constructs different graph views by using special un-shared parameters Siamese encoders for data augmentation, and learns the latent low-dimensional embedding representations via similarity-matrix-based contrastive learning. Comparative analyses of multiple scRNA-seq datasets from different platforms and with varying cell numbers demonstrate that scSCCNIA outperforms existing methods in terms of cell clustering and marker gene identification. Furthermore, scSCCNIA reveals the heterogeneity and functional specificity of various cell types through Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Overall, scSCCNIA is an effective algorithm for learning latent features from scRNA-seq data, enhancing cell type identification accuracy and facilitating downstream analyses of scRNA-seq data.

Single-cell multi-omics sequencing represents an advanced technology capable of simultaneously measuring multiple omics data from the same cell. The joint clustering of single-cell multi-omics sequencing data enables a comprehensive depiction of cell states and uncovers intricate molecular mechanisms, holding immense significance in fields such as oncology, neurology, and developmental biology. However, the disparities in feature spaces across different omics layers and data noise present substantial challenges for achieving accurate clustering. To tackle these challenges, we introduce a novel clustering method for single-cell multi-omics data, termed scMSAC, which is grounded in a denoising subgraph attention autoencoder. The proposed method employs a weighted nearest neighbor graph strategy to ascertain the weights of multi-omics data, subsequently generating a similarity graph that holistically encapsulates intercellular connections through the weighted amalgamation of diverse omics perspectives. The scMSAC model captures the topological features of cells through the subgraph attention autoencoder, constructing relationships among cells. For the omics features extracted by the subgraph attention autoencoder, scMSAC incorporates an SCA (Spatial Channel Attention) mechanism for feature fusion to reduce the differences in feature spaces of different omics and achieve better clustering performance. Comparative experiments with various existing methods demonstrate that scMSAC has excellent clustering performance and performs well in detecting rare cell types and differential expression analysis.

Convolutional Neural Networks struggle with long-range dependencies modeling in medical image segmentation, and traditional Transformer models rely on Multi-Layer Perceptron (MLP) for channel information mixing, with performance issues as data dimensions increase. These issues prompt a reassessment of the model's design to enhance segmentation performance and effectively capture long-range dependencies. Consequently, this study presents the Kadformer, a novel network optimized for fine-grained multi-organ segmentation. The Kadformer model adopts an innovative U-shaped network architecture, which enhances the extraction of spatial and channel features in the encoder through the KAN-Enhanced Multi-Dimensional Attention (KMA) mechanism, effectively compensating for information loss during downsampling. We design a Dynamic Path Selection (DPS) strategy to mitigate the feature extraction discrepancies encountered by the linear attention mechanism when processing category-sparse and category-dense images while enhancing feature discrimination through long-range sequential modeling Mamba. Furthermore, we construct the Data Interaction (DAI) module to guide the dual-path encoder's channel and spatial information filtering and effectively integrate the semantically inconsistent features between the KMA and DPS modules. Our approach achieves more than 30% parameter reduction compared to state-of-the-art methods. In addition, the Kadformer network outperforms existing segmentation methods on six public datasets, demonstrating excellent performance. The code has been made available on GitHub: https://github.com/wxc9927/Kadformer.

Infectious diseases continue to pose a serious threat to public health, underscoring the urgent need for effective computational approaches to screen novel anti-infective agents. Oligopeptides have emerged as promising candidates in an-timicrobial research due to their structural simplicity, high bioavailability, and low susceptibility to resistance. Despite their potential, computational models specifically designed to predict associations between oligopeptides and infectious diseases remain scarce. We propose Prompt-Guided Graph Contrastive Learning for Oligopeptide-Disease Association Prediction (PGCLODA), a framework designed to predict potential associations between oligopeptides and infectious diseases. A tripartite graph is constructed to integrate oligopeptides, microbes, and diseases. To preserve informative structures, we introduce a prompt-based graph augmentation strategy. Our dual encoder, composed of a graph convolutional network and a Transformer, captures both local and global features. The fused representations are used for final classification. Experiments on a benchmark dataset show that our method consistently outperforms existing models and demonstrates strong generalization capability in discovering novel associations.

In multi-organ segmentation tasks, both local details and global contextual information are crucial. Existing main-stream methods based on CNN-Transformer hybrid architectures typically employ simple serial stacking, end-stage concatenation, or pointwise addition for feature fusion, which struggle to handle feature inconsistency and often lead to information conflict and loss. To address the aforementioned challenges, we innovatively propose TBHF-Unet. We design a three-branch hierarchical encoder that dynamically fuses multi-source features in parallel, achieving deep layer-wise integration of multi-source information. The hierarchical structure maintains the independence of each branch while avoiding feature degradation, enabling superior performance without the need for excessively deep networks. Additionally, we design a Local-Global Feature Fusion (LGFF) module to efficiently and accurately integrate local details with global semantics, effectively alleviating feature inconsistency and achieving more comprehensive feature representation. Experiments on five public datasets demonstrate that the proposed method outperforms existing segmentation techniques, showing higher segmentation accuracy and robustness.

In recent years, antimicrobial peptides (AMPs) have attracted interest as potential peptide antibiotic due to their broad-spectrum antibacterial activity and high target specificity. However, existing research on AMP prediction mainly focuses on their functional properties, such as antibacterial, antiviral, and anticancer. This emphasis has created a significant gap in identifying AMPs that specifically target pathogens. Given the large variety of pathogens and the sparsity and imbalance of labels, it is challenging to determine which specific pathogens AMPs can effective against. To address this issue, we present PepXML, a large language model-based tool for extreme multilabel classification of pathogen-targeted AMPs. Our first step involved constructing a benchmark dataset of AMPs and their corresponding targeted pathogens, sourced from public databases. In PepXML, the peptides are embedded using ESM2. Further, clustering on a specifically designed label co-occurrence graph and hard negative sampling were employed to address challenges on data sparsity and label imbalance. To validate the reliability of our predictive results, we conducted molecular docking studies focused on peptide-bilayer membrane interactions and performed molecular dynamics simulations to elucidate the mechanisms of peptide-pathogen interactions. We anticipate that PepXML will be a valuable resource for advancing peptide-based therapeutics. The data and Python codes of the PepXML model are available at https://github.com/YannanBin/PepXML.git.

Single-cell RNA sequencing (scRNA-seq) enables the reconstruction of cell type-specific gene regulatory networks (GRNs), offering detailed insights into gene regulation at high resolution. While graph neural networks have become widely used for GRN inference, their message-passing mechanisms are often limited by issues such as over-smoothing and over-squashing, which hinder the preservation of essential network structure. To address these challenges, we propose a novel graph transformer-based model, AttentionGRN, which leverages soft encoding to enhance model expressiveness and improve the accuracy of GRN inference from scRNA-seq data. Furthermore, the GRN-oriented message aggregation strategies are designed to capture both the directed network structure information and functional information inherent in GRNs. Specifically, we design directed structure encoding to facilitate the learning of directed network topologies and employ functional gene sampling to capture key functional modules and global network structure. Our extensive experiments, conducted on 88 datasets across two distinct tasks, demonstrate that AttentionGRN consistently outperforms existing methods. Furthermore, AttentionGRN has been successfully applied to reconstruct cell type-specific GRNs for human mature hepatocytes, revealing novel hub genes and previously unidentified transcription factor-target gene regulatory associations.

The inappropriate use of antibiotics has precipitated the emergence of multidrug-resistant bacteria, prompting significant interest in antimicrobial peptides (AMPs) as potential alternatives to traditional antibiotics. Given the prohibitive costs and time-consuming nature of biological experiments, computational methods provide an efficient alternative for the development of AMP-based drugs. However, existing computational studies primarily focus on identifying AMPs with antimicrobial activity, lacking a targeted identification of AMPs against specific microbial species. To address this gap, we propose a peptide-microbe association (PMA) prediction framework, termed AEPMA, which is constructed based on an autoevolutionary heterogeneous graph. Within AEPMA, we construct an innovative peptide-microbe-disease network (PMDHAN). Furthermore, we design an autoevolutionary information aggregation mechanism that facilitates the representation learning of the heterogeneous graph. This model automatically aggregates semantic information within the heterogeneous network while thoroughly accounting for the spatiotemporal dependencies and heterogeneous interactions in the PMDHAN. Experiments conducted on one peptide-microbe and three drug-microbe association datasets demonstrate that the performance of AEPMA outperforms five state-of-the-art methods, demonstrating its robust modeling capability and exceptional generalization ability. In addition, this study identifies a novel anti-Staphylococcus aureus peptide and an anti-Escherichia coli peptide, thereby contributing valuable information for the development of antimicrobial drugs and strategies for mitigating antibiotic resistance.

Single-cell RNA sequencing (scRNA-seq) provides transcriptome profiling of individual cells, allowing for in-depth studies of cell heterogeneity at cell resolution. While cell clustering lays the basic foundation of scRNA-seq data analysis, the high-dimensionality and frequent dropout events of the data raise great challenges. Although plenty of dedicated clustering methods have been proposed, they often fail to fully explore the underlying data structure. Here, we introduce scMCGF, a new multi-view clustering algorithm based on graph fusion. It utilizes multi-view data generated from transcriptomic data to learn the consistent and complementary information across different view, ultimately constructing a unified graph matrix for robust cell clustering. Specifically, scMCGF utilizes two-dimensional-reduction methods (principal component analysis and diffusion maps) to capture both linear and non-linear characteristics of the data. Additionally, it calculates a cell-pathway score matrix to incorporate pathway-level information. These three features, along with the pre-processed gene expression data, form the multi-view data. scMCGF iteratively refines the structure of similarity graphs of each view through adaptive learning and learns a unified graph matrix by weighting and fusing the individual similarity graph matrix. The final clustering results are obtained by applying the rank constraint on the Laplacian matrix of the unified graph matrix. Experiments results of 13 real data sets reveal that scMCGF outperforms eight state-of-the-art methods in clustering accuracy and robustness. Furthermore, biological analysis validates that the clustering results of scMCGF provide a reliable foundation for downstream investigations.

Reconstructing high-resolution gene regulatory networks (GRNs) based on single-cell RNA sequencing data provides an opportunity to gain insight into disease pathogenesis. At present, there are a large number of GRN reconstruction methods based on graph neural networks, and they can obtain excellent performance in GRN inference by extracting network structure features. However, most of these methods fail to fully exploit the directional characteristics or even ignore them when extracting network structural features. To this end, a novel framework called GAEDGRN is proposed based on gravity-inspired graph autoencoder (GIGAE) to infer potential causal relationships between genes. Among them, GIGAE can help us capture the complex directed network topology in GRN. Additionally, due to the uneven distribution of the latent vectors generated by the graph autoencoder, a random walk-based method is used to regularize the latent vectors learnt by the encoder. Furthermore, considering that some genes in GRN usually have a significant impact on biological functions, GAEDGRN designs a gene importance score calculation method and pays attention to genes with high importance in the process of GRN reconstruction. Experimental results on seven cell types of three GRN types show that GAEDGRN achieves high accuracy and strong robustness. Moreover, a case study on human embryonic stem cells demonstrates that GAEDGRN can help identify important genes.

Molecular optimization, aiming to identify molecules with improved properties from a huge chemical search space, is a critical step in drug development. This task is challenging due to the need to optimize multiple properties while adhering to stringent drug-like criteria. Recently, numerous effective artificial intelligence methods have been proposed for molecular optimization. However, most of them neglect the constraints in molecular optimization, thereby limiting the development of high-quality molecules that simultaneously satisfy property objectives and constraint compliance. To address this issue, we proposed a deep multi-objective optimization framework, termed CMOMO, for constrained molecular multi-property optimization. The proposed CMOMO divides the optimization process into two stages, which enables it to use a dynamic constraint handling strategy to balance multi-property optimization and constraint satisfaction. Besides, a latent vector fragmentation based evolutionary reproduction strategy is designed to generate promising molecules effectively. Experimental results on two benchmark tasks show that the proposed CMOMO outperforms five state-of-the-art methods to obtain more successfully optimized molecules with multiple desired properties and satisfying drug-like constraints. Moreover, the superiority of CMOMO is verified on two practical tasks, including a potential protein-ligand optimization task of 4LDE protein, which is the structure of $\beta $2-adrenoceptor GPCR receptor, and a potential inhibitor optimization task of glycogen synthase kinase-3$\beta $ target (GSK3$\beta $). Notably, CMOMO demonstrates a two-fold improvement in success rate for the GSK3$\beta $ optimization task, successfully identifying molecules with favorable bioactivity, drug-likeness, synthetic accessibility, and adherence to structural constraints.

Cancer drug combination therapies offer a promising strategy to overcome resistance and improve treatment efficacy, but identifying synergistic drug pairs is challenging due to complex biological interactions and tumor heterogeneity. Current machine learning algorithms for drug synergy prediction primarily rely on large-scale, multimodal datasets, yet suffer from critical limitations including poor interpretability, difficulty distinguishing causative biological relationships from correlations, and inadequate modeling of cancer-specific molecular interactions. To address these challenges, we propose CASynergy (Causal Attention and Cross-attention Synergy), a novel deep learning model for predicting cancer drug synergy that addresses limitations of prior approaches in accuracy and interpretability. CASynergy introduces a causal attention mechanism to distinguish true causal genomic features from spurious correlations, cell line-specific gene network construction to capture the unique molecular context of each cancer cell line, and a cross-attention module to integrate drug molecular features with cell line gene expression profiles. These improvements allow CASynergy to clearly identify significant drug-gene interactions and provides interpretable insights into why a combination is predicted to be synergistic. Experiments on two benchmark datasets (DrugCombDB and Oncology-Screen) suggests that CASynergy outperformed five state-of-the-art models. CASynergy offers a better and more reliable way to predict effective drug combinations. It works well across different cancer types and is easier to understand, which is important for personalized cancer treatment and finding new drugs.

Synergistic drug combinations have shown promising results in treating cancer cell lines by enhancing therapeutic efficacy and minimizing adverse reactions. The effects of a drug vary across cell lines, and cell lines respond differently to various drugs during treatment. Recently, many AI-based techniques have been developed for predicting synergistic drug combinations. However, existing computational models have not addressed this phenomenon, neglecting the refinement of features for the same drug and cell line in different scenarios. In this work, we propose a feature refinement deep learning framework, termed FRSynergy, to identify synergistic drug combinations. It can guide the refinement of drug and cell line features in different scenarios by capturing relationships among diverse drug-drug-cell line triplet features and learning feature contextual information. The heterogeneous graph attention network is employed to acquire topological information-based original features for drugs and cell lines from sampled sub-graphs. Then, the feature refinement network is designed by combining attention mechanism and context information, which can learn context-aware feature representations for each drug and cell line feature in diverse drug-drug-cell line triplet contexts. Extensive experiments affirm the strong performance of FRSynergy in predicting synergistic drug combinations and, more importantly, demonstrate the effectiveness of feature refinement network in synergistic drug combination prediction.

The inference of gene regulatory networks (GRNs) from gene expression profiles has been a key issue in systems biology, prompting many researchers to develop diverse computational methods. However, most of these methods do not reconstruct directed GRNs with regulatory types because of the lack of benchmark datasets or defects in the computational methods. Here, we collect benchmark datasets and propose a deep learning-based model, DeepFGRN, for reconstructing fine gene regulatory networks (FGRNs) with both regulation types and directions. In addition, the GRNs of real species are always large graphs with direction and high sparsity, which impede the advancement of GRN inference. Therefore, DeepFGRN builds a node bidirectional representation module to capture the directed graph embedding representation of the GRN. Specifically, the source and target generators are designed to learn the low-dimensional dense embedding of the source and target neighbors of a gene, respectively. An adversarial learning strategy is applied to iteratively learn the real neighbors of each gene. In addition, because the expression profiles of genes with regulatory associations are correlative, a correlation analysis module is designed. Specifically, this module not only fully extracts gene expression features, but also captures the correlation between regulators and target genes. Experimental results show that DeepFGRN has a competitive capability for both GRN and FGRN inference. Potential biomarkers and therapeutic drugs for breast cancer, liver cancer, lung cancer and coronavirus disease 2019 are identified based on the candidate FGRNs, providing a possible opportunity to advance our knowledge of disease treatments.

Prediction of drug-target interactions (DTIs) is essential in medicine field, since it benefits the identification of molecular structures potentially interacting with drugs and facilitates the discovery and reposition of drugs. Recently, much attention has been attracted to network representation learning to learn rich information from heterogeneous data. Although network representation learning algorithms have achieved success in predicting DTI, several manually designed meta-graphs limit the capability of extracting complex semantic information. To address the problem, we introduce an adaptive meta-graph-based method, termed AMGDTI, for DTI prediction. In the proposed AMGDTI, the semantic information is automatically aggregated from a heterogeneous network by training an adaptive meta-graph, thereby achieving efficient information integration without requiring domain knowledge. The effectiveness of the proposed AMGDTI is verified on two benchmark datasets. Experimental results demonstrate that the AMGDTI method overall outperforms eight state-of-the-art methods in predicting DTI and achieves the accurate identification of novel DTIs. It is also verified that the adaptive meta-graph exhibits flexibility and effectively captures complex fine-grained semantic information, enabling the learning of intricate heterogeneous network topology and the inference of potential drug-target relationship.

Cluster assignment is vital to analyzing single-cell RNA sequencing (scRNA-seq) data to understand high-level biological processes. Deep learning-based clustering methods have recently been widely used in scRNA-seq data analysis. However, existing deep models often overlook the interconnections and interactions among network layers, leading to the loss of structural information within the network layers. Herein, we develop a new self-supervised clustering method based on an adaptive multi-scale autoencoder, called scAMAC. The self-supervised clustering network utilizes the Multi-Scale Attention mechanism to fuse the feature information from the encoder, hidden and decoder layers of the multi-scale autoencoder, which enables the exploration of cellular correlations within the same scale and captures deep features across different scales. The self-supervised clustering network calculates the membership matrix using the fused latent features and optimizes the clustering network based on the membership matrix. scAMAC employs an adaptive feedback mechanism to supervise the parameter updates of the multi-scale autoencoder, obtaining a more effective representation of cell features. scAMAC not only enables cell clustering but also performs data reconstruction through the decoding layer. Through extensive experiments, we demonstrate that scAMAC is superior to several advanced clustering and imputation methods in both data clustering and reconstruction. In addition, scAMAC is beneficial for downstream analysis, such as cell trajectory inference. Our scAMAC model codes are freely available at https://github.com/yancy2024/scAMAC.

Inferring gene regulatory network (GRN) is one of the important challenges in systems biology, and many outstanding computational methods have been proposed; however there remains some challenges especially in real datasets. In this study, we propose Directed Graph Convolutional neural network-based method for GRN inference (DGCGRN). To better understand and process the directed graph structure data of GRN, a directed graph convolutional neural network is conducted which retains the structural information of the directed graph while also making full use of neighbor node features. The local augmentation strategy is adopted in graph neural network to solve the problem of poor prediction accuracy caused by a large number of low-degree nodes in GRN. In addition, for real data such as E.coli, sequence features are obtained by extracting hidden features using Bi-GRU and calculating the statistical physicochemical characteristics of gene sequence. At the training stage, a dynamic update strategy is used to convert the obtained edge prediction scores into edge weights to guide the subsequent training process of the model. The results on synthetic benchmark datasets and real datasets show that the prediction performance of DGCGRN is significantly better than existing models. Furthermore, the case studies on bladder uroepithelial carcinoma and lung cancer cells also illustrate the performance of the proposed model.

MOTIVATION: In the drug development process, a significant portion of the budget and research time are dedicated to the lead compound optimization procedure to identify potential drugs. This procedure focuses on enhancing the pharmacological and bioactive properties of compounds by optimizing their local substructures. However, due to the vast and discrete chemical structure space and the unpredictable element combinations within this space, the optimization process is inherently complex. Various structure enumeration-based combinatorial optimization methods have shown certain advantages. However, they still have limitations. Those methods fail to consider the differences between molecules and struggle to explore the unknown outer search space. RESULTS: In this study, we propose an adaptive space search-based molecular evolution optimization algorithm (ASSMOEA). It consists of three key modules: construction of molecule-specific search space, molecular evolutionary optimization, and adaptive expansion of molecule-specific search space. Specifically, we design a fragment similarity tree in a molecule-specific search space and apply a dynamic mutation strategy in this space to guide molecular optimization. Then, we utilize an encoder-encoder structure to adaptively expand the space. Those three modules are circled iteratively to optimize molecules. Our experiments demonstrate that ASSMOEA outperforms existing methods in terms of molecular optimization. It not only enhances the efficiency of the molecular optimization process but also exhibits a robust ability to search for correct solutions. AVAILABILITY AND IMPLEMENTATION: The code is freely available on the web at https://github.com/bbbbb-b/MEOAFST.

This study focuses on building an intelligent decision-making attention mechanism in which the channel relationship and conduct feature maps among specific deep Dense ConvNet blocks are connected to each other. Thus, develop a novel freezing network with a pyramid spatial channel attention mechanism (FPSC-Net) in deep modeling. This model studies how specific design choices in the large-scale data-driven optimization and creation process affect the balance between the accuracy and effectiveness of the designed deep intelligent model. To this end, this study presents a novel architecture unit, which is termed as the "Activate-and-Freeze" block on popular and highly competitive datasets. In order to extract informative features by fusing spatial and channel-wise information together within local receptive fields and boost the representation power, this study constructs a Dense-attention module (pyramid spatial channel (PSC) attention) to perform feature recalibration, and through the PSC attention to model the interdependence among convolution feature channels. We join the PSC attention module in the activating and back-freezing strategy to search for one of the most important parts of the network for extraction and optimization. Experiments on various large-scale datasets demonstrate that the proposed method can achieve substantially better performance for improving the ConvNets representation power than the other state-of-the-art deep models.

Meter pointers exhibit stable and anti-interference capabilities, rendering them extensively utilized in industrial environments. However, automated reading poses a significant challenge due to the fact that current segmentation methods struggle to isolate the fine-grained pointers and scales for accurate reading calculations. This challenge can be alleviated by enhancing the feature extraction capability of the segmentation network. As is well-known that Attention plays an essential role in human vision by selectively focusing on convex parts, and attention-based methods have been applied to various computer vision tasks. Therefore, we propose a new image segmentation network called multipath attention network (MPANet) for pointer meter recognition in the complex industrial environments. The designed network employs an attention gate mechanism to proficiently capture local features stemming from various pathways during skip-connection and upsample processes. In addition, our network incorporates deep supervision by merging the outputs of the final three layers to extract abundant low-dimensional information. To further improve the performance of encoders and decoders, a residual U-block is employed, thereby forming an enhanced U-shaped network structure. In the experiments, we employ HD95, Dice, and Recall as evaluation metrics. MPANet demonstrates superior performance compared to state-of-the-art networks on three our self-collected datasets, showing improvements of over 1% across all metrics. In addition, we validate the efficacy of MPA as a plug-and-play module and the benefits of applying deep supervision to multidecoder network.

Synergistic drug combination prediction tasks based on the computational models have been widely studied and applied in the cancer field. However, most of models only consider the interactions between drug pairs and specific cell lines, without taking into account the multiple biological relationships of drug-drug and cell line-cell line that also largely affect synergistic mechanisms. To this end, here we propose a multi-modal deep learning framework, termed MDNNSyn, which adequately applies multi-source information and trains multi-modal features to infer potential synergistic drug combinations. MDNNSyn extracts topology modality features by implementing the multi-layer hypergraph neural network on drug synergy hypergraph and constructs semantic modality features through similarity strategy. A multi-modal fusion network layer with gated neural network is then employed for synergy score prediction. MDNNSyn is compared to five classic and state-of-the-art prediction methods on DrugCombDB and Oncology-Screen datasets. The model achieves area under the curve (AUC) scores of 0.8682 and 0.9013 on two datasets, an improvement of 3.70 % and 2.71 % over the second-best model. Case study indicates that MDNNSyn is capable of detecting potential synergistic drug combinations.

Recent methods often introduce attention mechanisms into the skip connections of U-shaped networks to capture features. However, these methods usually overlook spatial information extraction in skip connections and exhibit inefficiency in capturing spatial and channel information. This issue prompts us to reevaluate the design of the skip-connection mechanism and propose a new deep-learning network called the Fusing Spatial and Channel Attention Network, abbreviated as FSCA-Net. FSCA-Net is a novel U-shaped network architecture that utilizes the Parallel Attention Transformer (PAT) to enhance the extraction of spatial and channel features in the skip-connection mechanism, further compensating for downsampling losses. We design the Cross-Attention Bridge Layer (CAB) to mitigate excessive feature and resolution loss when downsampling to the lowest level, ensuring meaningful information fusion during upsampling at the lowest level. Finally, we construct the Dual-Path Channel Attention (DPCA) module to guide channel and spatial information filtering for Transformer features, eliminating ambiguities with decoder features and better concatenating features with semantic inconsistencies between the Transformer and the U-Net decoder. FSCA-Net is designed explicitly for fine-grained segmentation tasks of multiple organs and regions. Our approach achieves over 48% reduction in FLOPs and over 32% reduction in parameters compared to the state-of-the-art method. Moreover, FSCA-Net outperforms existing segmentation methods on seven public datasets, demonstrating exceptional performance.

A large number of works have presented the single-cell RNA sequencing (scRNA-seq) to study the diversity and biological functions of cells at the single-cell level. Clustering identifies unknown cell types, which is essential for downstream analysis of scRNA-seq samples. However, the high dimensionality, high noise and pervasive dropout rate of scRNA-seq samples have a significant challenge to the cluster analysis of scRNA-seq samples. Herein, we propose a new adaptive fuzzy clustering model based on the denoising autoencoder and self-attention mechanism called the scDASFK. It implements the comparative learning to integrate cell similar information into the clustering method and uses a deep denoising network module to denoise the data. scDASFK consists of a self-attention mechanism for further denoising where an adaptive clustering optimization function for iterative clustering is implemented. In order to make the denoised latent features better reflect the cell structure, we introduce a new adaptive feedback mechanism to supervise the denoising process through the clustering results. Experiments on 16 real scRNA-seq datasets show that scDASFK performs well in terms of clustering accuracy, scalability and stability. Overall, scDASFK is an effective clustering model with great potential for scRNA-seq samples analysis. Our scDASFK model codes are freely available at https://github.com/LRX2022/scDASFK.

The advances in single-cell ribonucleic acid sequencing (scRNA-seq) allow researchers to explore cellular heterogeneity and human diseases at cell resolution. Cell clustering is a prerequisite in scRNA-seq analysis since it can recognize cell identities. However, the high dimensionality, noises and significant sparsity of scRNA-seq data have made it a big challenge. Although many methods have emerged, they still fail to fully explore the intrinsic properties of cells and the relationship among cells, which seriously affects the downstream clustering performance. Here, we propose a new deep contrastive clustering algorithm called scDCCA. It integrates a denoising auto-encoder and a dual contrastive learning module into a deep clustering framework to extract valuable features and realize cell clustering. Specifically, to better characterize and learn data representations robustly, scDCCA utilizes a denoising Zero-Inflated Negative Binomial model-based auto-encoder to extract low-dimensional features. Meanwhile, scDCCA incorporates a dual contrastive learning module to capture the pairwise proximity of cells. By increasing the similarities between positive pairs and the differences between negative ones, the contrasts at both the instance and the cluster level help the model learn more discriminative features and achieve better cell segregation. Furthermore, scDCCA joins feature learning with clustering, which realizes representation learning and cell clustering in an end-to-end manner. Experimental results of 14 real datasets validate that scDCCA outperforms eight state-of-the-art methods in terms of accuracy, generalizability, scalability and efficiency. Cell visualization and biological analysis demonstrate that scDCCA significantly improves clustering and facilitates downstream analysis for scRNA-seq data. The code is available at https://github.com/WJ319/scDCCA.

The progress of single-cell RNA sequencing (scRNA-seq) has led to a large number of scRNA-seq data, which are widely used in biomedical research. The noise in the raw data and tens of thousands of genes pose a challenge to capture the real structure and effective information of scRNA-seq data. Most of the existing single-cell analysis methods assume that the low-dimensional embedding of the raw data belongs to a Gaussian distribution or a low-dimensional nonlinear space without any prior information, which limits the flexibility and controllability of the model to a great extent. In addition, many existing methods need high computational cost, which makes them difficult to be used to deal with large-scale datasets. Here, we design and develop a depth generation model named Gaussian mixture adversarial autoencoders (scGMAAE), assuming that the low-dimensional embedding of different types of cells follows different Gaussian distributions, integrating Bayesian variational inference and adversarial training, as to give the interpretable latent representation of complex data and discover the statistical distribution of different types of cells. The scGMAAE is provided with good controllability, interpretability and scalability. Therefore, it can process large-scale datasets in a short time and give competitive results. scGMAAE outperforms existing methods in several ways, including dimensionality reduction visualization, cell clustering, differential expression analysis and batch effect removal. Importantly, compared with most deep learning methods, scGMAAE requires less iterations to generate the best results.

Electric location-routing problem is a challenging problem consisting of the optimization of electric vehicle routing and charging facility location, simultaneously. Existing algorithms generally adopt the two-phase search strategy to alternately optimize the routing and the location. However, they are usually criticized for the inefficiency as the problem scale increases. In order to improve the search efficiency in each phase, we propose an accelerating two-phase multiobjective evolutionary algorithm, where the learning method is used to mine the useful information from the historical search process to generate the high-quality routing and location offspring. To be specific, in the routing optimization phase, an interpolation method is developed to extract the frequent visiting orders existing in the historical best solutions. These frequent visiting orders are used to create potential routing offspring that can accelerate the convergence toward the optimal solutions. In the location optimization phase, a surrogate model is used to approximatively represent the relationship from routing to location, which can directly output a promising location scheme for a given routing offspring and thus reduce the optimization time. Experimental results on different scales of test instances demonstrate the competitiveness of the proposed algorithm in comparison with several state of-the-art algorithms, including four widely used heuristic algorithms and two multiobjective evolutionary algorithms.

In recent years, synthesizing drugs powered by artificial intelligence has brought great convenience to society. Since retrosynthetic analysis occupies an essential position in synthetic chemistry, it has received broad attention from researchers. In this review, we comprehensively summarize the development process of retrosynthesis in the context of deep learning. This review covers all aspects of retrosynthesis, including datasets, models and tools. Specifically, we report representative models from academia, in addition to a detailed description of the available and stable platforms in the industry. We also discuss the disadvantages of the existing models and provide potential future trends, so that more abecedarians will quickly understand and participate in the family of retrosynthesis planning.

The rapid development of biomedicine has produced a large number of biomedical written materials. These unstructured text data create serious challenges for biomedical researchers to find information. Biomedical named entity recognition (BioNER) and biomedical relation extraction (BioRE) are the two most fundamental tasks of biomedical text mining. Accurately and efficiently identifying entities and extracting relations have become very important. Methods that perform two tasks separately are called pipeline models, and they have shortcomings such as insufficient interaction, low extraction quality and easy redundancy. To overcome the above shortcomings, many deep learning-based joint name entity recognition and relation extraction models have been proposed, and they have achieved advanced performance. This paper comprehensively summarize deep learning models for joint name entity recognition and relation extraction for biomedicine. The joint BioNER and BioRE models are discussed in the light of the challenges existing in the BioNER and BioRE tasks. Five joint BioNER and BioRE models and one pipeline model are selected for comparative experiments on four biomedical public datasets, and the experimental results are analyzed. Finally, we discuss the opportunities for future development of deep learning-based joint BioNER and BioRE models.

Rapid development of single-cell RNA sequencing (scRNA-seq) technology has allowed researchers to explore biological phenomena at the cellular scale. Clustering is a crucial and helpful step for researchers to study the heterogeneity of cell. Although many clustering methods have been proposed, massive dropout events and the curse of dimensionality in scRNA-seq data make it still difficult to analysis because they reduce the accuracy of clustering methods, leading to misidentification of cell types. In this work, we propose the scHFC, which is a hybrid fuzzy clustering method optimized by natural computation based on Fuzzy C Mean (FCM) and Gath-Geva (GG) algorithms. Specifically, principal component analysis algorithm is utilized to reduce the dimensions of scRNA-seq data after it is preprocessed. Then, FCM algorithm optimized by simulated annealing algorithm and genetic algorithm is applied to cluster the data to output a membership matrix, which represents the initial clustering result and is taken as the input for GG algorithm to get the final clustering results. We also develop a cluster number estimation method called multi-index comprehensive estimation, which can estimate the cluster numbers well by combining four clustering effectiveness indexes. The performance of the scHFC method is evaluated on 17 scRNA-seq datasets, and compared with six state-of-the-art methods. Experimental results validate the better performance of our scHFC method in terms of clustering accuracy and stability of algorithm. In short, scHFC is an effective method to cluster cells for scRNA-seq data, and it presents great potential for downstream analysis of scRNA-seq data. The source code is available at https://github.com/WJ319/scHFC.

MOTIVATION: A large number of studies have shown that clustering is a crucial step in scRNA-seq analysis. Most existing methods are based on unsupervised learning without the prior exploitation of any domain knowledge, which does not utilize available gold-standard labels. When confronted by the high dimensionality and general dropout events of scRNA-seq data, purely unsupervised clustering methods may not produce biologically interpretable clusters, which complicate cell type assignment. RESULTS: In this article, we propose a semi-supervised clustering method based on a capsule network named scCNC that integrates domain knowledge into the clustering step. Significantly, we also propose a Semi-supervised Greedy Iterative Training method used to train the whole network. Experiments on some real scRNA-seq datasets show that scCNC can significantly improve clustering performance and facilitate downstream analyses. AVAILABILITY AND IMPLEMENTATION: The source code of scCNC is freely available at https://github.com/WHY-17/scCNC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Single cell RNA sequencing (scRNA-seq) enables researchers to characterize transcriptomic profiles at the single-cell resolution with increasingly high throughput. Clustering is a crucial step in single cell analysis. Clustering analysis of transcriptome profiled by scRNA-seq can reveal the heterogeneity and diversity of cells. However, single cell study still remains great challenges due to its high noise and dimension. Subspace clustering aims at discovering the intrinsic structure of data in unsupervised fashion. In this paper, we propose a deep sparse subspace clustering method scDSSC combining noise reduction and dimensionality reduction for scRNA-seq data, which simultaneously learns feature representation and clustering via explicit modelling of scRNA-seq data generation. Experiments on a variety of scRNA-seq datasets from thousands to tens of thousands of cells have shown that scDSSC can significantly improve clustering performance and facilitate the interpretability of clustering and downstream analysis. Compared to some popular scRNA-deq analysis methods, scDSSC outperformed state-of-the-art methods under various clustering performance metrics.

Identifying functional modules in protein-protein interaction (PPI) networks elucidates cellular organization and mechanism. Various methods have been proposed to identify the functional modules in PPI networks, but most of these methods do not consider the noisy links in PPI networks. They achieve a competitive performance on the PPI networks without noisy links, but the performance of these methods considerably deteriorates in the noisy PPI networks. Furthermore, the noisy links are inevitable in the PPI networks. In this paper, we propose a novel link-driven label propagation algorithm (LLPA) to identify functional modules in PPI networks. The LLPA first find link clusters in PPI networks, and then the functional modules are identified from the link clusters. Two strategies aimed to ensure the robustness of LLPA are proposed. One strategy involves the proposed LLPA updating the link labels in accordance with the designed weight of the link, which can reduce the incidence of noisy links. The other strategy involves the filtration of some noisy labels from the link clusters to further reduce the influence of noisy links. The performance evaluation on three real PPI networks shows that LLPA outperforms other eight state-of-the-art detection algorithms in terms of accuracy and robustness.

Identifying cell types is one of the main goals of single-cell RNA sequencing (scRNA-seq) analysis, and clustering is a common method for this item. However, the massive amount of data and the excess noise level bring challenge for single cell clustering. To address this challenge, in this paper, we introduced a novel method named single-cell clustering based on denoising autoencoder and graph convolution network (scCDG), which consists of two core models. The first model is a denoising autoencoder (DAE) used to fit the data distribution for data denoising. The second model is a graph autoencoder using graph convolution network (GCN), which projects the data into a low-dimensional space (compressed) preserving topological structure information and feature information in scRNA-seq data simultaneously. Extensive analysis on seven real scRNA-seq datasets demonstrate that scCDG outperforms state-of-the-art methods in some research sub-fields, including single cell clustering, visualization of transcriptome landscape, and trajectory inference.

Lots of experimental studies have revealed the significant associations between lncRNAs and diseases. Identifying accurate associations will provide a new perspective for disease therapy. Calculation-based methods have been developed to solve these problems, but these methods have some limitations. In this paper, we proposed an accurate method, named MLGCNET, to discover potential lncRNA-disease associations. Firstly, we reconstructed similarity networks for both lncRNAs and diseases using top k similar information, and constructed a lncRNA-disease heterogeneous network (LDN). Then, we applied Multi-Layer Graph Convolutional Network on LDN to obtain latent feature representations of nodes. Finally, the Extra Trees was used to calculate the probability of association between disease and lncRNA. The results of extensive 5-fold cross-validation experiments show that MLGCNET has superior prediction performance compared to the state-of-the-art methods. Case studies confirm the performance of our model on specific diseases. All the experiment results prove the effectiveness and practicality of MLGCNET in predicting potential lncRNA-disease associations.

Both objective optimization and constraint satisfaction are crucial for solving constrained multiobjective optimization problems, but the existing evolutionary algorithms encounter difficulties in striking a good balance between them when tackling complex feasible regions. To address this issue, this article proposes a two-stage evolutionary algorithm, which adjusts the fitness evaluation strategies during the evolutionary process to adaptively balance objective optimization and constraint satisfaction. The proposed algorithm can switch between the two stages according to the status of the current population, enabling the population to cross the infeasible region and reach the feasible regions in one stage, and to spread along the feasible boundaries in the other stage. Experimental studies on four benchmark suites and three real-world applications demonstrate the superiority of the proposed algorithm over the state-of-the-art algorithms, especially on problems with complex feasible regions.

In silico reuse of old drugs (also known as drug repositioning) to treat common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked drugs, with potentially lower overall development costs and shorter development timelines. Therefore, there is a pressing need for computational drug repurposing methodologies to facilitate drug discovery. In this study, we propose a new method, called DRHGCN (Drug Repositioning based on the Heterogeneous information fusion Graph Convolutional Network), to discover potential drugs for a certain disease. To make full use of different topology information in different domains (i.e. drug-drug similarity, disease-disease similarity and drug-disease association networks), we first design inter- and intra-domain feature extraction modules by applying graph convolution operations to the networks to learn the embedding of drugs and diseases, instead of simply integrating the three networks into a heterogeneous network. Afterwards, we parallelly fuse the inter- and intra-domain embeddings to obtain the more representative embeddings of drug and disease. Lastly, we introduce a layer attention mechanism to combine embeddings from multiple graph convolution layers for further improving the prediction performance. We find that DRHGCN achieves high performance (the average AUROC is 0.934 and the average AUPR is 0.539) in four benchmark datasets, outperforming the current approaches. Importantly, we conducted molecular docking experiments on DRHGCN-predicted candidate drugs, providing several novel approved drugs for Alzheimer's disease (e.g. benzatropine) and Parkinson's disease (e.g. trihexyphenidyl and haloperidol).

The traditional covert communication that relies on a central node is vulnerable to detection and attack. Applying blockchain to covert communication can improve the channel's anti-interference and antitampering. Whisper is the communication protocol of Ethereum, which mainly relies on payload to store information and padding to expand. These two fields can store a large amount of information, creating conditions for the realization of covert communication. In this paper, we propose a covert communication method based on the whisper protocol to covertly transfer information in the blockchain. To implement this method, we use payload to store the carrier information, matching it with the secret message. The generated index is recorded in the padding field. To improve the concealment of communication, we simulate the default filling rules of the protocol to maintain the message size. A new topic–key pair interaction method is also proposed to improve the security of the model. Moreover, the anti-interference, antitampering and antidetection of the newly proposed model are verified through theoretical analysis and experiment. The experimental findings show that the amount of information in the proposed method is 4.7 times that of the traditional time-based covert communication. The time consumption of information transmission is reduced to 52.25% under the same settings and even less in actual use. The cost of the new topic–key pair interaction is reduced by nearly 50% compared with the original method.

miRNAs belong to small non-coding RNAs that are related to a number of complicated biological processes. Considerable studies have suggested that miRNAs are closely associated with many human diseases. In this study, we proposed a computational model based on Similarity Constrained Matrix Factorization for miRNA-Disease Association Prediction (SCMFMDA). In order to effectively combine different disease and miRNA similarity data, we applied similarity network fusion algorithm to obtain integrated disease similarity (composed of disease functional similarity, disease semantic similarity and disease Gaussian interaction profile kernel similarity) and integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity and miRNA Gaussian interaction profile kernel similarity). In addition, the L2 regularization terms and similarity constraint terms were added to traditional Nonnegative Matrix Factorization algorithm to predict disease-related miRNAs. SCMFMDA achieved AUCs of 0.9675 and 0.9447 based on global Leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, the case studies on two common human diseases were also implemented to demonstrate the prediction accuracy of SCMFMDA. The out of top 50 predicted miRNAs confirmed by experimental reports that indicated SCMFMDA was effective for prediction of relationship between miRNAs and diseases.

microRNAs (miRNAs) are small non-coding RNAs related to a number of complicated biological processes. A growing body of studies have suggested that miRNAs are closely associated with many human diseases. It is meaningful to consider disease-related miRNAs as potential biomarkers, which could greatly contribute to understanding the mechanisms of complex diseases and benefit the prevention, detection, diagnosis and treatment of extraordinary diseases. In this study, we presented a novel model named Graph Convolutional Autoencoder for miRNA-Disease Association Prediction (GCAEMDA). In the proposed model, we utilized miRNA-miRNA similarities, disease-disease similarities and verified miRNA-disease associations to construct a heterogeneous network, which is applied to learn the embeddings of miRNAs and diseases. In addition, we separately constructed miRNA-based and disease-based sub-networks. Combining the embeddings of miRNAs and diseases, graph convolutional autoencoder (GCAE) was utilized to calculate association scores of miRNA-disease on two sub-networks, respectively. Furthermore, we obtained final prediction scores between miRNAs and diseases by adopting an average ensemble way to integrate the prediction scores from two types of subnetworks. To indicate the accuracy of GCAEMDA, we applied different cross validation methods to evaluate our model whose performances were better than the state-of-the-art models. Case studies on a common human diseases were also implemented to prove the effectiveness of GCAEMDA. The results demonstrated that GCAEMDA was beneficial to infer potential associations of miRNA-disease.

Community detection has been recognized as one of the most important tools to discover useful information hidden in complex networks which is usually hard to be obtained by simple observations. Existing community detection algorithms have demonstrated their effectiveness on a variety of complex networks, most of them, however, suffer from the scalability issue on complex networks without a clear community structure due to the challenge in the detection of ambiguous community structure. To address this issue, in this paper, we propose a community structure enhancement method, termed CSE, for community detection in complex networks. In the proposed CSE, the community structure of a network is enhanced by adding links between the nodes possibly belonging to the same community and reducing links between those belonging to different communities, thereby converting an ambiguous community structure into a structure much clearer than the original one. The experimental results show the superior performance of the proposed CSE over five state-of-the-art community detection algorithms on both synthetic benchmark networks and real-world networks, especially for those without a clear community structure.

Both convergence and diversity are crucial to evolutionary many-objective optimization, whereas most existing dominance relations show poor performance in balancing them, thus easily leading to a set of solutions concentrating on a small region of the Pareto fronts. In this paper, a novel dominance relation is proposed to better balance convergence and diversity for evolutionary many-objective optimization. In the proposed dominance relation, an adaptive niching technique is developed based on the angles between the candidate solutions, where only the best converged candidate solution is identified to be nondominated in each niche. Experimental results demonstrate that the proposed dominance relation outperforms existing dominance relations in balancing convergence and diversity. A modified NSGA-II is suggested based on the proposed dominance relation, which shows competitiveness against the state-of-the-art algorithms in solving many-objective optimization problems. The effectiveness of the proposed dominance relation is also verified on several other existing multi- and many-objective evolutionary algorithms.

Spiking neural P systems with rules on synapses (RSSN P systems, for short) are a class of distributed and parallel computation models inspired by the way in which neurons process and communicate information with each other by means of spikes, where neurons only contain spikes and the evolution rules are on synapses. RSSN P systems have been proved to be Turing universal, using the strategy that restricts all the applied rules to consume the same number of spikes from the given neuron, termed as equal spikes consumption strategy. In this work, in order to avoid imposing the equal spikes consumption restriction on the application of rules, a new strategy for rule application, termed as sum spikes consumption strategy, is considered in RSSN P systems, where a maximal set of enabled rules from synapses starting from the same neuron is nondeterministically chosen to be applied, in the sense that no further synapse can use any of its rules, and the sum of these numbers of spikes that all the applied rules consume is removed from the neuron. In this way, the proposed strategy avoids checking whether all the applied rules consume the same number of spikes from the given neuron. The computation power of RSSN P systems working in the proposed strategy is investigated, and it is proved that such systems characterize the semilinear sets of natural numbers, i.e., such systems are not universal. Furthermore, RSSN P systems with weighted synapses working in the proposed strategy are proved to be Turing universal. These results show that the weight on synapses is a powerful ingredient of RSSN P systems in terms of the computation power, which makes RSSN P systems working in sum spikes consumption strategy become universal from non-universality.

Community detection is an important tool to analyze hidden information such as functional module and topology structure in complex networks. Compared with traditional community detection, it is more challenging to find overlapping communities in complex networks, especially when the networks are of large scales. Among various overlapping community detection techniques, the well-known clique percolation method (CPM) has shown promising performance in terms of quality of found communities, but suffers from serious curse of dimensionality due to its high computational complexity, which makes it very unlikely to be applied to large-scale networks. To address this issue, in this paper, we propose a weak-CPM for overlapping community detection in large-scale networks. A new measure for characterizing the similarity between weak cliques is also suggested to check whether the weak cliques can be merged into a community. Experimental results on synthetic and realworld networks demonstrate the competitive performance of the proposed method over six popular overlapping community detection algorithms in terms of both computational efficiency and quality of found communities. In addition, the proposed method is also suitable for detecting large-scale networks with an unclear community structure under different levels of overlapping density and overlapping diversity, which is an important property of many real-world complex networks.

Designing multiobjective evolutionary algorithms (MOEAs) for community detection in complex networks has attracted much attention of researchers recently. However, most of the existing methods focus on addressing the task of nonoverlapping community detection, where each node must belong to one and only one community. In fact, communities are often overlapped with each other in many real-world networks, thus it is necessary to design overlapping community detection algorithms. To this end, this paper proposes a mixed representation-based MOEA (MR-MOEA) for overlapping community detection. In MR-MOEA, a mixed individual representation scheme is proposed to fast encode and decode the overlapping divisions of complex networks. Specifically, this mixed representation consists of two parts: one represents all potential overlapping nodes and the other delegates all nonoverlapping nodes. These two parts evolve together to detect the overlapping communities of networks based on different updating strategies suggested in MR-MOEA. We verify the effectiveness of the proposed algorithm MR-MOEA on ten real-world complex networks and the experimental results demonstrate that MR-MOEA is superior over six representative algorithms for overlapping community detection.

Non-small cell lung cancer (NSCLC) is a malignant tumor, and contains three major subtypes which are difficult to be distinguished at early stages of NSCLC. Many pathways work together to perform certain functions in cells. One might expect the high level of co-appearance or repression of pathways to distinguish different subtypes of NSCLC. However, it is difficult to detect coordinated regulations of pathways by existing methods. In our work, the coordinated regulations of pathways are detected using modified higher logic analysis of gene expression data. Specifically, we identify the genes whose regulation obeys a logic function by the modified higher logic analysis, which focuses on the relationships among the gene triplets that are not evident when genes are examined in a pairwise fashion. Then, the relationships among genes are mapped to pathways to predict the coordinated regulated relationships among pathways. By comparing coordinated regulations of pathways, we find that the regulation patterns of pathways which are associated with cell death are different in three subtypes of NSCLC. This method allows us to uncover co-appearance or repression of pathways in high level, and it has a potential to distinguish the subtypes for complex diseases.