NIDM: network impulsive dynamics on multiplex biological network for disease-gene prediction

Function-Genes and Disease-Genes Prediction Based on Network Embedding and One-Class Classification

Using genes which have been experimentally-validated for diseases (functions) can develop machine learning methods to predict new disease/function-genes. However, the prediction of both function-genes and disease-genes faces the same problem: there are only certain positive examples, but no negative examples. To solve this problem, we proposed a function/disease-genes prediction algorithm based on network embedding (Variational Graph Auto-Encoders, VGAE) and one-class classification (Fast Minimum Covariance Determinant, Fast-MCD): VGAEMCD. Firstly, we constructed a protein-protein interaction (PPI) network centered on experimentally-validated genes; then VGAE was used to get the embeddings of nodes (genes) in the network; finally, the embeddings were input into the improved deep learning one-class classifier based on Fast-MCD to predict function/disease-genes. VGAEMCD can predict function-gene and disease-gene in a unified way, and only the experimentally-verified genes are needed to provide (no need for expression profile). VGAEMCD outperforms classical one-class classification algorithms in Recall, Precision, F-measure, Specificity, and Accuracy. Further experiments show that seven metrics of VGAEMCD are higher than those of state-of-art function/disease-genes prediction algorithms. The above results indicate that VGAEMCD can well learn the distribution characteristics of positive examples and accurately identify function/disease-genes.

MDDOmics: multi-omics resource of major depressive disorder

Major depressive disorder (MDD) is a pressing global health issue. Its pathogenesis remains elusive, but numerous studies have revealed its intricate associations with various biological factors. Consequently, there is an urgent need for a comprehensive multi-omics resource to help researchers in conducting multi-omics data analysis for MDD. To address this issue, we constructed the MDDOmics database (Major Depressive Disorder Omics, (https://www.csuligroup.com/MDDOmics/), which integrates an extensive collection of published multi-omics data related to MDD. The database contains 41 222 entries of MDD research results and several original datasets, including Single Nucleotide Polymorphisms, genes, non-coding RNAs, DNA methylations, metabolites and proteins, and offers various interfaces for searching and visualization. We also provide extensive downstream analyses of the collected MDD data, including differential analysis, enrichment analysis and disease-gene prediction. Moreover, the database also incorporates multi-omics data for bipolar disorder, schizophrenia and anxiety disorder, due to the challenge in differentiating MDD from similar psychiatric disorders. In conclusion, by leveraging the rich content and online interfaces from MDDOmics, researchers can conduct more comprehensive analyses of MDD and its similar disorders from various perspectives, thereby gaining a deeper understanding of potential MDD biomarkers and intricate disease pathogenesis. Database URL: https://www.csuligroup.com/MDDOmics/.

PGAGP: Predicting pathogenic genes based on adaptive network embedding algorithm

The study of disease-gene associations is an important topic in the field of computational biology. The accumulation of massive amounts of biomedical data provides new possibilities for exploring potential relations between diseases and genes through computational strategy, but how to extract valuable information from the data to predict pathogenic genes accurately and rapidly is currently a challenging and meaningful task. Therefore, we present a novel computational method called PGAGP for inferring potential pathogenic genes based on an adaptive network embedding algorithm. The PGAGP algorithm is to first extract initial features of nodes from a heterogeneous network of diseases and genes efficiently and effectively by Gaussian random projection and then optimize the features of nodes by an adaptive refining process. These low-dimensional features are used to improve the disease-gene heterogenous network, and we apply network propagation to the improved heterogenous network to predict pathogenic genes more effectively. By a series of experiments, we study the effect of PGAGP's parameters and integrated strategies on predictive performance and confirm that PGAGP is better than the state-of-the-art algorithms. Case studies show that many of the predicted candidate genes for specific diseases have been implied to be related to these diseases by literature verification and enrichment analysis, which further verifies the effectiveness of PGAGP. Overall, this work provides a useful solution for mining disease-gene heterogeneous network to predict pathogenic genes more effectively.

Disease-gene prediction based on preserving structure network embedding

Many diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD), are caused by abnormalities or mutations of related genes. Many computational methods based on the network relationship between diseases and genes have been proposed to predict potential pathogenic genes. However, how to effectively mine the disease-gene relationship network to predict disease genes better is still an open problem. In this paper, a disease-gene-prediction method based on preserving structure network embedding (PSNE) is introduced. In order to predict pathogenic genes more effectively, a heterogeneous network with multiple types of bio-entities was constructed by integrating disease-gene associations, human protein network, and disease-disease associations. Furthermore, the low-dimension features of nodes extracted from the network were used to reconstruct a new disease-gene heterogeneous network. Compared with other advanced methods, the performance of PSNE has been confirmed more effective in disease-gene prediction. Finally, we applied the PSNE method to predict potential pathogenic genes for age-associated diseases such as AD and PD. We verified the effectiveness of these predicted potential genes by literature verification. Overall, this work provides an effective method for disease-gene prediction, and a series of high-confidence potential pathogenic genes of AD and PD which may be helpful for the experimental discovery of disease genes.

Drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization

Coronavirus disease 2019 (COVID-19), a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently spreading rapidly around the world. Since SARS-CoV-2 seriously threatens human life and health as well as the development of the world economy, it is very urgent to identify effective drugs against this virus. However, traditional methods to develop new drugs are costly and time-consuming, which makes drug repositioning a promising exploration direction for this purpose. In this study, we collected known antiviral drugs to form five virus-drug association datasets, and then explored drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization (VDA-GKSBMF). By the 5-fold cross-validation, we found that VDA-GKSBMF has an area under curve (AUC) value of 0.8851, 0.8594, 0.8807, 0.8824, and 0.8804, respectively, on the five datasets, which are higher than those of other state-of-art algorithms in four datasets. Based on known virus-drug association data, we used VDA-GKSBMF to prioritize the top-k candidate antiviral drugs that are most likely to be effective against SARS-CoV-2. We confirmed that the top-10 drugs can be molecularly docked with virus spikes protein/human ACE2 by AutoDock on five datasets. Among them, four antiviral drugs ribavirin, remdesivir, oseltamivir, and zidovudine have been under clinical trials or supported in recent literatures. The results suggest that VDA-GKSBMF is an effective algorithm for identifying potential antiviral drugs against SARS-CoV-2.

Inferring Latent MicroRNA-Disease Associations on a Gene-Mediated Tripartite Heterogeneous Multiplexing Network

MicroRNA (miRNA) is a class of non-coding single-stranded RNA molecules encoded by endogenous genes with a length of about 22 nucleotides. MiRNAs have been successfully identified as differentially expressed in various cancers. There is evidence that disorders of miRNAs are associated with a variety of complex diseases. Therefore, inferring potential miRNA-disease associations (MDAs) is very important for understanding the aetiology and pathogenesis of many diseases and is useful to disease diagnosis, prognosis and treatment. First, We creatively fused multiple similarity subnetworks from multi-sources for miRNAs, genes and diseases by multiplexing technology, respectively. Then, three multiplexed biological subnetworks are connected through the extended binary association to form a tripartite complete heterogeneous multiplexed network (Tri-HM). Finally, because the constructed Tri-HM network can retain subnetworks' original topology and biological functions and expands the binary association and dependence between the three biological entities, rich neighbourhood information is obtained iteratively from neighbours by a non-equilibrium random walk. Through cross-validation, our tri-HM-RWR model obtained an AUC value of 0.8657, and an AUPR value of 0.2139 in the global 5-fold cross-validation, which shows that our model can more fully speculate disease-related miRNAs.

SVMMDR: Prediction of miRNAs-drug resistance using support vector machines based on heterogeneous network

In recent years, the miRNA is considered as a potential high-value therapeutic target because of its complex and delicate mechanism of gene regulation. The abnormal expression of miRNA can cause drug resistance, affecting the therapeutic effect of the disease. Revealing the associations between miRNAs-drug resistance can help in the design of effective drugs or possible drug combinations. However, current conventional experiments for identification of miRNAs-drug resistance are time-consuming and high-cost. Therefore, it’s of pretty realistic value to develop an accurate and efficient computational method to predicting miRNAs-drug resistance. In this paper, a method based on the Support Vector Machines (SVM) to predict the association between MiRNA and Drug Resistance (SVMMDR) is proposed. The SVMMDR integrates miRNAs-drug resistance association, miRNAs sequence similarity, drug chemical structure similarity and other similarities, extracts path-based Hetesim features, and obtains inclined diffusion feature through restart random walk. By combining the multiple feature, the prediction score between miRNAs and drug resistance is obtained based on the SVM. The innovation of the SVMMDR is that the inclined diffusion feature is obtained by inclined restart random walk, the node information and path information in heterogeneous network are integrated, and the SVM is used to predict potential miRNAs-drug resistance associations. The average AUC of SVMMDR obtained is 0.978 in 10-fold cross-validation.

HyMM: hybrid method for disease-gene prediction by integrating multiscale module structure

MOTIVATION

Identifying disease-related genes is an important issue in computational biology. Module structure widely exists in biomolecule networks, and complex diseases are usually thought to be caused by perturbations of local neighborhoods in the networks, which can provide useful insights for the study of disease-related genes. However, the mining and effective utilization of the module structure is still challenging in such issues as a disease gene prediction.

RESULTS

We propose a hybrid disease-gene prediction method integrating multiscale module structure (HyMM), which can utilize multiscale information from local to global structure to more effectively predict disease-related genes. HyMM extracts module partitions from local to global scales by multiscale modularity optimization with exponential sampling, and estimates the disease relatedness of genes in partitions by the abundance of disease-related genes within modules. Then, a probabilistic model for integration of gene rankings is designed in order to integrate multiple predictions derived from multiscale module partitions and network propagation, and a parameter estimation strategy based on functional information is proposed to further enhance HyMM's predictive power. By a series of experiments, we reveal the importance of module partitions at different scales, and verify the stable and good performance of HyMM compared with eight other state-of-the-arts and its further performance improvement derived from the parameter estimation.

CONCLUSIONS

The results confirm that HyMM is an effective framework for integrating multiscale module structure to enhance the ability to predict disease-related genes, which may provide useful insights for the study of the multiscale module structure and its application in such issues as a disease-gene prediction.

SEPA: Signalling entropy-based algorithm to evaluate personalized pathway activation for survival analysis on pan-cancer data

MOTIVATION

Biomarkers with prognostic ability and biological interpretability can be used to support decision-making in the survival analysis. Genes usually form functional modules to play synergistic roles, such as pathways. Predicting significant features from the functional level can effectively reduce the adverse effects of heterogeneity and obtain more reproducible and interpretable biomarkers. Personalized pathway activation inference can quantify the dysregulation of essential pathways involved in the initiation and progression of cancers, and can contribute to the development of personalized medical treatments.

RESULTS

In this study, we propose a novel method to evaluate personalized pathway activation based on signalling entropy for survival analysis (SEPA), which is a new attempt to introduce the information-theoretic entropy in generating pathway representation for each patient. SEPA effectively integrates pathway-level information into gene expression data, converting the high-dimensional gene expression data into the low-dimensional biological pathway activation scores. SEPA shows its classification power on the prognostic pan-cancer genomic data, and the potential pathway markers identified based on SEPA have statistical significance in the discrimination of high-risk and low-risk cohorts and are likely to be associated with the initiation and progress of cancers. The results show that SEPA scores can be used as an indicator to precisely distinguish cancer patients with different clinical outcomes, and identify important pathway features with strong discriminative power and biological interpretability.

AVAILABILITY

The MATLAB-package for SEPA is freely available from https://github.com/xingyili/SEPA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

SVPath: an accurate pipeline for predicting the pathogenicity of human exon structural variants

Although there are a large number of structural variations in the chromosomes of each individual, there is a lack of more accurate methods for identifying clinical pathogenic variants. Here, we proposed SVPath, a machine learning-based method to predict the pathogenicity of deletions, insertions and duplications structural variations that occur in exons. We constructed three types of annotation features for each structural variation event in the ClinVar database. First, we treated complex structural variations as multiple consecutive single nucleotide polymorphisms events, and annotated them with correlation scores based on single nucleic acid substitutions, such as the impact on protein function. Second, we determined which genes the variation occurred in, and constructed gene-based annotation features for each structural variation. Third, we also calculated related features based on the transcriptome, such as histone signal, the overlap ratio of variation and genomic element definitions, etc. Finally, we employed a gradient boosting decision tree machine learning method, and used the deletions, insertions and duplications in the ClinVar database to train a structural variation pathogenicity prediction model SVPath. These structural variations are clearly indicated as pathogenic or benign. Experimental results show that our SVPath has achieved excellent predictive performance and outperforms existing state-of-the-art tools. SVPath is very promising in evaluating the clinical pathogenicity of structural variants. SVPath can be used in clinical research to predict the clinical significance of unknown pathogenicity and new structural variation, so as to explore the relationship between diseases and structural variations in a computational way.

Biomedical data, computational methods and tools for evaluating disease-disease associations

In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.

Predicting LncRNA-Disease Association by a Random Walk With Restart on Multiplex and Heterogeneous Networks

Studies have found that long non-coding RNAs (lncRNAs) play important roles in many human biological processes, and it is critical to explore potential lncRNA–disease associations, especially cancer-associated lncRNAs. However, traditional biological experiments are costly and time-consuming, so it is of great significance to develop effective computational models. We developed a random walk algorithm with restart on multiplex and heterogeneous networks of lncRNAs and diseases to predict lncRNA–disease associations (MHRWRLDA). First, multiple disease similarity networks are constructed by using different approaches to calculate similarity scores between diseases, and multiple lncRNA similarity networks are also constructed by using different approaches to calculate similarity scores between lncRNAs. Then, a multiplex and heterogeneous network was constructed by integrating multiple disease similarity networks and multiple lncRNA similarity networks with the lncRNA–disease associations, and a random walk with restart on the multiplex and heterogeneous network was performed to predict lncRNA–disease associations. The results of Leave-One-Out cross-validation (LOOCV) showed that the value of Area under the curve (AUC) was 0.68736, which was improved compared with the classical algorithm in recent years. Finally, we confirmed a few novel predicted lncRNAs associated with specific diseases like colon cancer by literature mining. In summary, MHRWRLDA contributes to predict lncRNA–disease associations.

Candidate gene prioritization for chronic obstructive pulmonary disease using expression information in protein-protein interaction networks

Background

Identifying or prioritizing genes for chronic obstructive pulmonary disease (COPD), one type of complex disease, is particularly important for its prevention and treatment.

Methods

In this paper, a novel method was proposed to Prioritize genes using Expression information in Protein–protein interaction networks with disease risks transferred between genes (abbreviated as PEP). A weighted COPD PPI network was constructed using expression information and then COPD candidate genes were prioritized based on their corresponding disease risk scores in descending order.

Results

Further analysis demonstrated that the PEP method was robust in prioritizing disease candidate genes, and superior to other existing prioritization methods exploiting either topological or functional information. Top-ranked COPD candidate genes and their significantly enriched functions were verified to be related to COPD. The top 200 candidate genes might be potential disease genes in the diagnosis and treatment of COPD.

Conclusions

The proposed method could provide new insights to the research of prioritizing candidate genes of COPD or other complex diseases with expression information from sequencing or microarray data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-021-01646-9.

Predicting LncRNA–Disease Association by a Random Walk With Restart on Multiplex and Heterogeneous Networks

Studies have found that long non-coding RNAs (lncRNAs) play important roles in many human biological processes, and it is critical to explore potential lncRNA–disease associations, especially cancer-associated lncRNAs. However, traditional biological experiments are costly and time-consuming, so it is of great significance to develop effective computational models. We developed a random walk algorithm with restart on multiplex and heterogeneous networks of lncRNAs and diseases to predict lncRNA–disease associations (MHRWRLDA). First, multiple disease similarity networks are constructed by using different approaches to calculate similarity scores between diseases, and multiple lncRNA similarity networks are also constructed by using different approaches to calculate similarity scores between lncRNAs. Then, a multiplex and heterogeneous network was constructed by integrating multiple disease similarity networks and multiple lncRNA similarity networks with the lncRNA–disease associations, and a random walk with restart on the multiplex and heterogeneous network was performed to predict lncRNA–disease associations. The results of Leave-One-Out cross-validation (LOOCV) showed that the value of Area under the curve (AUC) was 0.68736, which was improved compared with the classical algorithm in recent years. Finally, we confirmed a few novel predicted lncRNAs associated with specific diseases like colon cancer by literature mining. In summary, MHRWRLDA contributes to predict lncRNA–disease associations.