Heterogeneous information network and its application to human health and disease

The molecular components with the functional interdependencies in human cell form complicated biological network. Diseases are mostly caused by the perturbations of the composite of the interaction multi-biomolecules, rather than an abnormality of a single biomolecule. Furthermore, new biological functions and processes could be revealed by discovering novel biological entity relationships. Hence, more and more biologists focus on studying the complex biological system instead of the individual biological components. The emergence of heterogeneous information network (HIN) offers a promising way to systematically explore complicated and heterogeneous relationships between various molecules for apparently distinct phenotypes. In this review, we first present the basic definition of HIN and the biological system considered as a complex HIN. Then, we discuss the topological properties of HIN and how these can be applied to detect network motif and functional module. Afterwards, methodologies of discovering relationships between disease and biomolecule are presented. Useful insights on how HIN aids in drug development and explores human interactome are provided. Finally, we analyze the challenges and opportunities for uncovering combinatorial patterns among pharmacogenomics and cell-type detection based on single-cell genomic data.

[1]  Paul J Hergenrother,et al.  Targeting RNA with small molecules. , 2008, Chemical reviews.

[2]  Hui Liu,et al.  Inferring new indications for approved drugs via random walk on drug-disease heterogenous networks , 2016, BMC Bioinformatics.

[3]  Hui Xiao,et al.  NONCODE v3.0: integrative annotation of long noncoding RNAs , 2011, Nucleic Acids Res..

[4]  B. Barlogie,et al.  Antitumor activity of thalidomide in refractory multiple myeloma. , 1999, The New England journal of medicine.

[5]  T. Katsuya,et al.  Genetic variants at the 9p21 locus contribute to atherosclerosis through modulation of ANRIL and CDKN2A/B. , 2012, Atherosclerosis.

[6]  Eric E Schadt,et al.  The role of macromolecular damage in aging and age-related disease. , 2014, The journals of gerontology. Series A, Biological sciences and medical sciences.

[7]  Xing Chen,et al.  Long non-coding RNAs and complex diseases: from experimental results to computational models , 2016, Briefings Bioinform..

[8]  Cheng Liang,et al.  Semi-supervised prediction of human miRNA-disease association based on graph regularization framework in heterogeneous networks , 2018, Neurocomputing.

[9]  Xing Chen,et al.  Semi-supervised learning for potential human microRNA-disease associations inference , 2014, Scientific Reports.

[10]  Anton J. Enright,et al.  An efficient algorithm for large-scale detection of protein families. , 2002, Nucleic acids research.

[11]  A. Hopkins Network pharmacology: the next paradigm in drug discovery. , 2008, Nature chemical biology.

[12]  Jiawei Luo,et al.  A Novel Cluster-Based Computational Method to Identify miRNA Regulatory Modules , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[13]  Trey Ideker,et al.  Boosting Signal-to-Noise in Complex Biology: Prior Knowledge Is Power , 2011, Cell.

[14]  A. Barabasi,et al.  Network medicine--from obesity to the "diseasome". , 2007, The New England journal of medicine.

[15]  Xing Chen,et al.  Inferring potential small molecule–miRNA association based on triple layer heterogeneous network , 2018, Journal of Cheminformatics.

[16]  M. Oti,et al.  The modular nature of genetic diseases , 2006, Clinical genetics.

[17]  David S. Wishart,et al.  HMDB 4.0: the human metabolome database for 2018 , 2017, Nucleic Acids Res..

[18]  Xing Chen,et al.  In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm , 2018, Molecular therapy. Nucleic acids.

[19]  Xiangrong Liu,et al.  deepDR: a network-based deep learning approach to in silico drug repositioning , 2019, Bioinform..

[20]  Sebastian Wernicke,et al.  FANMOD: a tool for fast network motif detection , 2006, Bioinform..

[21]  Hyeong Jun An,et al.  Estimating the size of the human interactome , 2008, Proceedings of the National Academy of Sciences.

[22]  Jörg Rademann,et al.  Lysosomal Pathology and Osteopetrosis upon Loss of H+-Driven Lysosomal Cl– Accumulation , 2010, Science.

[23]  Yang Li,et al.  HMDD v2.0: a database for experimentally supported human microRNA and disease associations , 2013, Nucleic Acids Res..

[24]  Chee Keong Kwoh,et al.  Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization , 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[25]  Juan Liu,et al.  A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules , 2011, Bioinform..

[26]  Wei Lin,et al.  A comprehensive overview and evaluation of circular RNA detection tools , 2017, PLoS Comput. Biol..

[27]  Ao Li,et al.  Relevance search for predicting lncRNA-protein interactions based on heterogeneous network , 2016, Neurocomputing.

[28]  Xin Yao,et al.  Modularity-based credible prediction of disease genes and detection of disease subtypes on the phenotype-gene heterogeneous network , 2011, BMC Systems Biology.

[29]  Qianlan Yao,et al.  Prioritizing Candidate Disease Metabolites Based on Global Functional Relationships between Metabolites in the Context of Metabolic Pathways , 2014, PloS one.

[30]  Decheng Yang,et al.  MicroRNA: an Emerging Therapeutic Target and Intervention Tool , 2008, International journal of molecular sciences.

[31]  Ka-Chun Wong,et al.  ToBio: Global Pathway Similarity Search Based on Topological and Biological Features , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[32]  Yongdong Zhang,et al.  Drug-target interaction prediction: databases, web servers and computational models , 2016, Briefings Bioinform..

[33]  Xiangxiang Zeng,et al.  Inferring MicroRNA-Disease Associations by Random Walk on a Heterogeneous Network with Multiple Data Sources , 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[34]  Sune Lehmann,et al.  Link communities reveal multiscale complexity in networks , 2009, Nature.

[35]  Ao Li,et al.  A Heterogeneous Network Based Method for Identifying GBM-Related Genes by Integrating Multi-Dimensional Data , 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[36]  J. Lehár,et al.  Multi-target therapeutics: when the whole is greater than the sum of the parts. , 2007, Drug discovery today.

[37]  Cheng Liang,et al.  A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations , 2018, Bioinform..

[38]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[39]  Zuping Zhang,et al.  miRDDCR: a miRNA-based method to comprehensively infer drug-disease causal relationships , 2017, Scientific Reports.

[40]  Tingting Fu,et al.  Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics , 2017, Nucleic Acids Res..

[41]  Jing Li,et al.  dbDEPC 2.0: updated database of differentially expressed proteins in human cancers , 2011, Nucleic Acids Res..

[42]  Yi Pan,et al.  miRTRS: A Recommendation Algorithm for Predicting miRNA Targets , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[43]  Lei Wang,et al.  BNPMDA: Bipartite Network Projection for MiRNA–Disease Association prediction , 2018, Bioinform..

[44]  Yitzhak Pilpel,et al.  Global and Local Architecture of the Mammalian microRNA–Transcription Factor Regulatory Network , 2007, PLoS Comput. Biol..

[45]  Xing Chen,et al.  PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction , 2017, PLoS Comput. Biol..

[46]  Yingming Zhao,et al.  Modification‐specific proteomics: Strategies for characterization of post‐translational modifications using enrichment techniques , 2009, Proteomics.

[47]  Junwei Han,et al.  Global Prioritization of Disease Candidate Metabolites Based on a Multi-omics Composite Network , 2015, Scientific Reports.

[48]  Xing Chen,et al.  LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction , 2017, PLoS Comput. Biol..

[49]  B. Al-Lazikani,et al.  Combinatorial drug therapy for cancer in the post-genomic era , 2012, Nature Biotechnology.

[50]  C. Ponting,et al.  Evolution and Functions of Long Noncoding RNAs , 2009, Cell.

[51]  Cheng Liang,et al.  A novel motif-discovery algorithm to identify co-regulatory motifs in large transcription factor and microRNA co-regulatory networks in human , 2015, Bioinform..

[52]  An-Yuan Guo,et al.  Transcription factor and microRNA co-regulatory loops: important regulatory motifs in biological processes and diseases , 2015, Briefings Bioinform..

[53]  Wei Huang,et al.  A Novel Approach to Identify the miRNA-mRNA Causal Regulatory Modules in Cancer , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[54]  Kinya Otsu,et al.  Macromolecular Degradation Systems and Cardiovascular Aging. , 2016, Circulation research.

[55]  Artemis G. Hatzigeorgiou,et al.  DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA–gene interactions , 2017, Nucleic Acids Res..

[56]  Albertha J. M. Walhout,et al.  The interplay between transcription factors and microRNAs in genome‐scale regulatory networks , 2009, BioEssays : news and reviews in molecular, cellular and developmental biology.

[57]  Jiawei Luo,et al.  A novel approach for predicting microRNA-disease associations by unbalanced bi-random walk on heterogeneous network , 2017, J. Biomed. Informatics.

[58]  Michelle R. Arkin,et al.  Small-molecule inhibitors of protein–protein interactions: progressing towards the dream , 2004, Nature Reviews Drug Discovery.

[59]  Hyeon-Eui Kim,et al.  Deep mining heterogeneous networks of biomedical linked data to predict novel drug‐target associations , 2017, Bioinform..

[60]  R. Kuang,et al.  Network-based Phenome-Genome Association Prediction by Bi-Random Walk , 2015, PloS one.

[61]  T. Chou Drug combination studies and their synergy quantification using the Chou-Talalay method. , 2010, Cancer research.

[62]  Lin He,et al.  MicroRNAs: small RNAs with a big role in gene regulation , 2004, Nature reviews genetics.

[63]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

[64]  U. Alon Network motifs: theory and experimental approaches , 2007, Nature Reviews Genetics.

[65]  Xing Chen,et al.  EGBMMDA: Extreme Gradient Boosting Machine for MiRNA-Disease Association prediction , 2018, Cell Death & Disease.

[66]  Qinghua Guo,et al.  LncRNA2Target v2.0: a comprehensive database for target genes of lncRNAs in human and mouse , 2018, Nucleic Acids Res..

[67]  Dapeng Hao,et al.  Prioritizing candidate disease-related long non-coding RNAs by walking on the heterogeneous lncRNA and disease network. , 2015, Molecular bioSystems.

[68]  Ana Kozomara,et al.  miRBase: from microRNA sequences to function , 2018, Nucleic Acids Res..

[69]  Jinyan Li,et al.  Disease gene identification by random walk on multigraphs merging heterogeneous genomic and phenotype data , 2012, BMC Genomics.

[70]  Vipin Kumar,et al.  Co-clustering phenome–genome for phenotype classification and disease gene discovery , 2012, Nucleic acids research.

[71]  Xin Chen,et al.  DCDB 2.0: a major update of the drug combination database , 2014, Database J. Biol. Databases Curation.

[72]  Cheng Liang,et al.  A novel semi-supervised model for miRNA-disease association prediction based on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{d , 2018, Journal of Translational Medicine.

[73]  David S. Wishart,et al.  DrugBank 5.0: a major update to the DrugBank database for 2018 , 2017, Nucleic Acids Res..

[74]  J. Mattick The Genetic Signatures of Noncoding RNAs , 2009, PLoS genetics.

[75]  Xiangxiang Zeng,et al.  Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks , 2016, Briefings Bioinform..

[76]  Zhi-yi Zhang,et al.  Biotransformation and in vitro assessment of metabolism-associated drug-drug interaction for CRx-102, a novel combination drug candidate. , 2009, Journal of pharmaceutical and biomedical analysis.

[77]  Xing Chen,et al.  MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction , 2018, PLoS Comput. Biol..

[78]  A. Barabasi,et al.  The human disease network , 2007, Proceedings of the National Academy of Sciences.

[79]  Xiangxiang Zeng,et al.  Probability-based collaborative filtering model for predicting gene–disease associations , 2017, BMC Medical Genomics.

[80]  Xing Chen,et al.  NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning , 2016, PLoS Comput. Biol..

[81]  Haiyuan Yu,et al.  Detecting overlapping protein complexes in protein-protein interaction networks , 2012, Nature Methods.

[82]  Jingpu Zhang,et al.  Integrating Multiple Heterogeneous Networks for Novel LncRNA-Disease Association Inference , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[83]  Ivan G. Costa,et al.  A multiple kernel learning algorithm for drug-target interaction prediction , 2016, BMC Bioinformatics.

[84]  Svetlana A. Shabalina,et al.  Negative Correlation between Expression Level and Evolutionary Rate of Long Intergenic Noncoding RNAs , 2011, Genome biology and evolution.

[85]  Ariel Chernomoretz,et al.  A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases , 2016, PLoS neglected tropical diseases.

[86]  Yi Pan,et al.  SinNLRR: a robust subspace clustering method for cell type detection by non-negative and low-rank representation , 2019, Bioinform..

[87]  Jiawei Luo,et al.  A path-based measurement for human miRNA functional similarities using miRNA-disease associations , 2016, Scientific Reports.

[88]  Kate Traynor FDA approves four-drug anti-HIV combination tablet. , 2012, American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists.

[89]  Jing Wang,et al.  Identifying novel associations between small molecules and miRNAs based on integrated molecular networks , 2015, Bioinform..

[90]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[91]  Zhiwei Cao,et al.  Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer , 2015, Nature Communications.

[92]  Rory Johnson Long non-coding RNAs in Huntington's disease neurodegeneration , 2012, Neurobiology of Disease.

[93]  Xing Chen,et al.  ASDCD: Antifungal Synergistic Drug Combination Database , 2014, PloS one.

[94]  David S. Wishart,et al.  Chapter 3: Small Molecules and Disease , 2012, PLoS Comput. Biol..

[95]  Duncan Fyfe Gillies,et al.  A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data , 2015, Adv. Bioinformatics.

[96]  Jian-Yu Shi,et al.  Predicting binary, discrete and continued lncRNA-disease associations via a unified framework based on graph regression , 2017, BMC Medical Genomics.

[97]  D Hare,et al.  The Orange Book: the Food and Drug Administration's advice on therapeutic equivalence. , 1990, American pharmacy.

[98]  Ao Li,et al.  Discovery of Bladder Cancer-related Genes Using Integrative Heterogeneous Network Modeling of Multi-omics Data , 2017, Scientific Reports.

[99]  Shi-Hua Zhang,et al.  Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data , 2016, Bioinform..

[100]  P. Pandolfi,et al.  Complete remission after treatment of acute promyelocytic leukemia with arsenic trioxide. , 1998, The New England journal of medicine.

[101]  Ao Li,et al.  A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions , 2016, Genom. Proteom. Bioinform..

[102]  Jinyan Li,et al.  Laplacian normalization and random walk on heterogeneous networks for disease-gene prioritization , 2015, Comput. Biol. Chem..

[103]  S. Scheindlin,et al.  Rare diseases, orphan drugs, and orphaned patients. , 2006, Molecular interventions.

[104]  Na-Na Guan,et al.  Predicting miRNA‐disease association based on inductive matrix completion , 2018, Bioinform..

[105]  S. Kauppinen,et al.  Therapeutic Silencing of MicroRNA-122 in Primates with Chronic Hepatitis C Virus Infection , 2010, Science.

[106]  Ao Li,et al.  A novel method for identifying potential disease-related miRNAs via a disease-miRNA-target heterogeneous network. , 2017, Molecular bioSystems.

[107]  D. Kell Metabolomics and systems biology: making sense of the soup. , 2004, Current opinion in microbiology.

[108]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[109]  Aedín C. Culhane,et al.  A multivariate approach to the integration of multi-omics datasets , 2014, BMC Bioinformatics.

[110]  D. Wilson,et al.  Interaction of amiloride and hydrochlorothiazide with atrial natriuretic factor in the medullary collecting duct. , 1988, Canadian journal of physiology and pharmacology.

[111]  A. van Oudenaarden,et al.  MicroRNA-mediated feedback and feedforward loops are recurrent network motifs in mammals. , 2007, Molecular cell.

[112]  Yi Pan,et al.  Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm , 2016, Bioinform..

[113]  Bridget E. Begg,et al.  A Proteome-Scale Map of the Human Interactome Network , 2014, Cell.

[114]  J. Frank Managing hypertension using combination therapy. , 2008, American family physician.

[115]  J. Mattick,et al.  Long non-coding RNAs: insights into functions , 2009, Nature Reviews Genetics.

[116]  Xia Li,et al.  Prediction of potential disease-associated microRNAs based on random walk , 2015, Bioinform..

[117]  Thomas J. Jentsch,et al.  Endosomal Chloride-Proton Exchange Rather Than Chloride Conductance Is Crucial for Renal Endocytosis , 2010, Science.

[118]  Xiao-Ying Yan,et al.  Prediction of drug-target interaction by label propagation with mutual interaction information derived from heterogeneous network. , 2016, Molecular bioSystems.

[119]  Albert-László Barabási,et al.  A Dynamic Network Approach for the Study of Human Phenotypes , 2009, PLoS Comput. Biol..

[120]  M. Ritchie,et al.  Methods of integrating data to uncover genotype–phenotype interactions , 2015, Nature Reviews Genetics.

[121]  Thomas C. Wiegers,et al.  The Comparative Toxicogenomics Database: update 2017 , 2016, Nucleic Acids Res..

[122]  Weiwen Zhang,et al.  Integrating multiple 'omics' analysis for microbial biology: application and methodologies. , 2010, Microbiology.

[123]  Yi Zheng,et al.  Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite , 2017, BMC Bioinformatics.

[124]  Panayiotis V. Benos,et al.  mirConnX: condition-specific mRNA-microRNA network integrator , 2011, Nucleic Acids Res..

[125]  Souvik Maiti,et al.  The tuberculosis drug streptomycin as a potential cancer therapeutic: inhibition of miR-21 function by directly targeting its precursor. , 2012, Angewandte Chemie.

[126]  Xiangxiang Zeng,et al.  Prediction and validation of association between microRNAs and diseases by multipath methods. , 2016, Biochimica et biophysica acta.

[127]  S. Cohen,et al.  MicroRNAs and gene regulatory networks: managing the impact of noise in biological systems. , 2010, Genes & development.

[128]  Alan F. Scott,et al.  Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders , 2004, Nucleic Acids Res..

[129]  A. Barabasi,et al.  An empirical framework for binary interactome mapping , 2008, Nature Methods.

[130]  Alfonso Rodríguez-Patón,et al.  Meta-Path Methods for Prioritizing Candidate Disease miRNAs , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[131]  J. Lindon,et al.  Systems biology: Metabonomics , 2008, Nature.

[132]  Xiangxiang Zeng,et al.  Prediction and Validation of Disease Genes Using HeteSim Scores , 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[133]  T M Grogan,et al.  Comparison of a standard regimen (CHOP) with three intensive chemotherapy regimens for advanced non-Hodgkin's lymphoma. , 1993, The New England journal of medicine.

[134]  Li Wang,et al.  Lnc2Cancer v2.0: updated database of experimentally supported long non-coding RNAs in human cancers , 2018, Nucleic Acids Res..

[135]  Xing Chen,et al.  Novel human lncRNA-disease association inference based on lncRNA expression profiles , 2013, Bioinform..

[136]  Giovanni Vanni Frajese,et al.  The Inhibition of the Highly Expressed Mir-221 and Mir-222 Impairs the Growth of Prostate Carcinoma Xenografts in Mice , 2008, PloS one.

[137]  J. V. Moran,et al.  Initial sequencing and analysis of the human genome. , 2001, Nature.

[138]  Yadong Wang,et al.  miR2Disease: a manually curated database for microRNA deregulation in human disease , 2008, Nucleic Acids Res..

[139]  M. Gerstein,et al.  Getting connected: analysis and principles of biological networks. , 2007, Genes & development.

[140]  Xing Chen,et al.  MicroRNAs and complex diseases: from experimental results to computational models , 2019, Briefings Bioinform..

[141]  Preetam Ghosh,et al.  DISMIRA: Prioritization of disease candidates in miRNA-disease associations based on maximum weighted matching inference model and motif-based analysis , 2015, BMC Genomics.

[142]  Xinxia Peng,et al.  Computational identification of hepatitis C virus associated microRNA-mRNA regulatory modules in human livers , 2009, BMC Genomics.

[143]  Yusuke Nakamura,et al.  Association of a novel long non‐coding RNA in 8q24 with prostate cancer susceptibility , 2011, Cancer science.

[144]  R. Shiekhattar,et al.  Regulation of transcription by long noncoding RNAs. , 2014, Annual review of genetics.

[145]  E. Schadt Molecular networks as sensors and drivers of common human diseases , 2009, Nature.

[146]  Mark Gerstein,et al.  Bridging structural biology and genomics: assessing protein interaction data with known complexes. , 2002, Drug discovery today.

[147]  Xing Chen,et al.  MCMDA: Matrix completion for MiRNA-disease association prediction , 2017, Oncotarget.

[148]  Xia Li,et al.  SM2miR: a database of the experimentally validated small molecules' effects on microRNA expression , 2013, Bioinform..

[149]  R. Jiang Walking on multiple disease-gene networks to prioritize candidate genes. , 2015, Journal of molecular cell biology.

[150]  Jiahui Liu,et al.  Prioritizing disease genes with an improved dual label propagation framework , 2018, BMC Bioinformatics.

[151]  Yaohang Li,et al.  Computational drug repositioning using low-rank matrix approximation and randomized algorithms , 2018, Bioinform..

[152]  Nicholas T. Ingolia,et al.  Mammalian microRNAs predominantly act to decrease target mRNA levels , 2010, Nature.

[153]  Hailin Chen,et al.  A Semi-Supervised Method for Drug-Target Interaction Prediction with Consistency in Networks , 2013, PloS one.

[154]  Guangyuan Fu,et al.  BRWLDA: bi-random walks for predicting lncRNA-disease associations , 2017, Oncotarget.

[155]  Junhyong Kim,et al.  The promise of single-cell sequencing , 2013, Nature Methods.

[156]  A. Barabasi,et al.  Network medicine : a network-based approach to human disease , 2010 .

[157]  S. Ekins,et al.  In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling , 2007, British journal of pharmacology.

[158]  Ashis Kumer Biswas,et al.  Robust Inductive Matrix Completion Strategy to Explore Associations Between LincRNAs and Human Disease Phenotypes , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[159]  Chunyan Miao,et al.  Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction , 2016, PLoS Comput. Biol..

[160]  Royston Goodacre,et al.  Metabolomics: Current technologies and future trends , 2006, Proteomics.

[161]  Tapio Pahikkala,et al.  Toward more realistic drug^target interaction predictions , 2014 .

[162]  Armando Blanco,et al.  DrugNet: Network-based drug-disease prioritization by integrating heterogeneous data , 2015, Artif. Intell. Medicine.

[163]  Chee-Keong Kwoh,et al.  Ensemble Prediction of Synergistic Drug Combinations Incorporating Biological, Chemical, Pharmacological, and Network Knowledge , 2019, IEEE Journal of Biomedical and Health Informatics.

[164]  Susumu Goto,et al.  KEGG for integration and interpretation of large-scale molecular data sets , 2011, Nucleic Acids Res..

[165]  Cheng Liang,et al.  Discovering Synergistic Drug Combination from a Computational Perspective. , 2018, Current topics in medicinal chemistry.

[166]  E. Dermitzakis,et al.  Tissue-Specific Effects of Genetic and Epigenetic Variation on Gene Regulation and Splicing , 2015, PLoS genetics.

[167]  Yixue Li,et al.  Global Prioritizing Disease Candidate lncRNAs via a Multi-level Composite Network , 2017, Scientific Reports.

[168]  Yanli Wang,et al.  Predicting drug-target interactions by dual-network integrated logistic matrix factorization , 2017, Scientific Reports.

[169]  R. Jiang,et al.  Integrating human omics data to prioritize candidate genes , 2013, BMC Medical Genomics.

[170]  Georgios A. Pavlopoulos,et al.  Bipartite graphs in systems biology and medicine: a survey of methods and applications , 2018, GigaScience.

[171]  Jiawei Luo,et al.  Identifying Functional Modules in Co-Regulatory Networks Through Overlapping Spectral Clustering , 2018, IEEE Transactions on NanoBioscience.

[172]  Weixiong Zhang,et al.  A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer network , 2017, Bioinform..

[173]  Govindaraju Archunan,et al.  MicroRNAs -the Next Generation Therapeutic Targets in Human Diseases , 2013, Theranostics.

[174]  Cheng Liang,et al.  A Novel Method to Detect Functional microRNA Regulatory Modules by Bicliques Merging , 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[175]  Cheng Liang,et al.  Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs , 2019, PLoS Comput. Biol..

[176]  S. Shen-Orr,et al.  Network motifs: simple building blocks of complex networks. , 2002, Science.

[177]  Min Li,et al.  Heterogeneous Network Model to Infer Human Disease-Long Intergenic Non-Coding RNA Associations , 2015, IEEE Transactions on NanoBioscience.

[178]  Sayan Mukherjee,et al.  Sustained-input switches for transcription factors and microRNAs are central building blocks of eukaryotic gene circuits , 2013, Genome Biology.

[179]  Bin Chen,et al.  Predicting drug target interactions using meta-path-based semantic network analysis , 2016, BMC Bioinformatics.

[180]  J. Lindon,et al.  Metabonomics: a platform for studying drug toxicity and gene function , 2002, Nature Reviews Drug Discovery.

[181]  C. Arenz,et al.  miRNAs as novel therapeutic targets and diagnostic biomarkers for Parkinson’s disease: a patent evaluation of WO2014018650 , 2014, Expert opinion on therapeutic patents.

[182]  Weixiong Zhang,et al.  Inferring Disease-Associated microRNAs in Heterogeneous Networks with Node Attributes , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[183]  Philip S. Yu,et al.  HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks , 2013, IEEE Transactions on Knowledge and Data Engineering.

[184]  Cheng Liang,et al.  Human disease MiRNA inference by combining target information based on heterogeneous manifolds , 2018, J. Biomed. Informatics.

[185]  Margaret S. Ebert,et al.  Roles for MicroRNAs in Conferring Robustness to Biological Processes , 2012, Cell.

[186]  Cheng Liang,et al.  A Novel Group Wise-Based Method for Calculating Human miRNA Functional Similarity , 2017, IEEE Access.

[187]  Shulin Wang,et al.  Feature selection in machine learning: A new perspective , 2018, Neurocomputing.

[188]  Tao Jiang,et al.  Uncover disease genes by maximizing information flow in the phenome–interactome network , 2011, Bioinform..

[189]  R. Albert Scale-free networks in cell biology , 2005, Journal of Cell Science.

[190]  T. Ashburn,et al.  Drug repositioning: identifying and developing new uses for existing drugs , 2004, Nature Reviews Drug Discovery.

[191]  Joshua A. Grochow,et al.  Network Motif Discovery Using Subgraph Enumeration and Symmetry-Breaking , 2007, RECOMB.

[192]  Jiuyong Li,et al.  Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy , 2009, BMC Bioinformatics.

[193]  Jiawei Luo,et al.  A Meta-Path-Based Prediction Method for Human miRNA-Target Association , 2016, BioMed research international.

[194]  Zhen Yang,et al.  LncRNADisease 2.0: an updated database of long non-coding RNA-associated diseases , 2018, Nucleic Acids Res..

[195]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.