A Review of Recent Developments and Progress in Computational Drug Repositioning

Author(s): Wanwan Shi, Xuegong Chen, Lei Deng*

Journal Name: Current Pharmaceutical Design

Volume 26 , Issue 26 , 2020

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Computational drug repositioning is an efficient approach towards discovering new indications for existing drugs. In recent years, with the accumulation of online health-related information and the extensive use of biomedical databases, computational drug repositioning approaches have achieved significant progress in drug discovery. In this review, we summarize recent advancements in drug repositioning. Firstly, we explicitly demonstrated the available data source information which is conducive to identifying novel indications. Furthermore, we provide a summary of the commonly used computing approaches. For each method, we briefly described techniques, case studies, and evaluation criteria. Finally, we discuss the limitations of the existing computing approaches.

Keywords: Computational drug repositioning, drug-disease association, indication, biological network, machine learning, sparse matrix, text mining.

Dickson M, Gagnon JP. The cost of new drug discovery and development. Discov Med 2004; 4(22): 172-9.
[PMID: 20704981]
Shaughnessy AF. Old drugs, new tricks. BMJ 2011; 342: d741.
[http://dx.doi.org/10.1136/bmj.d741] [PMID: 21307112]
Shameer K, Readhead B, Dudley JT. Computational and experimental advances in drug repositioning for accelerated therapeutic stratification. Curr Top Med Chem 2015; 15(1): 5-20.
[http://dx.doi.org/10.2174/1568026615666150112103510] [PMID: 25579574]
Ashburn TT, Thor KBJNRDD. Drug repositioning: identifying and developing new uses for existing drugs 2004; ; 3: 673-683.
Bolgár B, Arany Á, Temesi G, Balogh B, Antal P, Mátyus P. Drug repositioning for treatment of movement disorders: from serendipity to rational discovery strategies. Curr Top Med Chem 2013; 13(18): 2337-63.
[http://dx.doi.org/10.2174/15680266113136660164] [PMID: 24059461]
Dudley JT, Deshpande T, Butte AJ. Exploiting drug-disease relationships for computational drug repositioning. Brief Bioinform 2011; 12(4): 303-11.
[http://dx.doi.org/10.1093/bib/bbr013] [PMID: 21690101]
Keiser MJ, Setola V, Irwin JJ, et al. Predicting new molecular targets for known drugs. Nature 2009; 462(7270): 175-81.
[http://dx.doi.org/10.1038/nature08506] [PMID: 19881490]
Ha S, Seo Y-J, Kwon M-S, Chang B-H, Han C-K, Yoon J-H. IDMap: facilitating the detection of potential leads with therapeutic targets. Bioinformatics 2008; 24(11): 1413-5.
[http://dx.doi.org/10.1093/bioinformatics/btn138] [PMID: 18417489]
von Eichborn J, Murgueitio MS, Dunkel M, Koerner S, Bourne PE, Preissner R. PROMISCUOUS: a database for network-based drug-repositioning. Nucleic Acids Res 2011; 39(Database issue): D1060-6.
[http://dx.doi.org/10.1093/nar/gkq1037] [PMID: 21071407]
Zhang W, Yue X, Lin W, et al. Predicting drug-disease associations by using similarity constrained matrix factorization. BMC Bioinformatics 2018; 19(1): 233.
[http://dx.doi.org/10.1186/s12859-018-2220-4] [PMID: 29914348]
Luo H, Li M, Wang S, Liu Q, Li Y, Wang J. Computational drug repositioning using low-rank matrix approximation and randomized algorithms. Bioinformatics 2018; 34(11): 1904-12.
[http://dx.doi.org/10.1093/bioinformatics/bty013] [PMID: 29365057]
Liu H, Luo LB, Cheng ZZ, et al. Group-sparse modeling drug-kinase networks for predicting combinatorial drug sensitivity in cancer cells. Curr Bioinform 2018; 13: 437-43.
Su R, Liu X, Wei L, Zou Q. Deep-Resp-Forest: A deep forest model to predict anti-cancer drug response. Methods 2019; 166: 91-102.
[http://dx.doi.org/10.1016/j.ymeth.2019.02.009] [PMID: 30772464]
Yu L, Sun X, Tian SW, Shi XY, Yan YL. Drug and nondrug classification based on deep learning with various feature selection strategies. Curr Bioinform 2018; 13: 253-9.
Zhu XJ, Feng CQ, Lai HY, Chen W, Lin H. Predicting protein structural classes for low-similarity sequences by evaluating different features. Knowl Base Syst 2019; 163: 787-93.
Feng P, Zhang J, Tang H, Chen W, Lin H. Predicting the organelle location of noncoding RNAs using pseudo nucleotide compositions. Interdiscip Sci 2017; 9(4): 540-4.
[PMID: 27739055]
Chen LJ, Zou B, Lee VHF, Yan H. Analysis of the relative movements between EGFR and drug inhibitors based on molecular dynamics simulation. Curr Bioinform 2018; 13: 299-309.
Fathima AJ, Murugaboopathi G, Selvam P. Pharmacophore mapping of ligand based virtual screening, molecular docking and molecular dynamic simulation studies for finding potent NS2B/NS3 protease inhibitors as potential anti-dengue drug compounds. Curr Bioinform 2018; 13: 606-16.
Cheng L, Jiang Y, Ju H, et al. InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk. BMC Genomics 2018; 19(Suppl. 1): 919.
[http://dx.doi.org/10.1186/s12864-017-4338-6] [PMID: 29363423]
Cheng L, Hu Y, Sun J, Zhou M, Jiang Q. DincRNA: a comprehensive web-based bioinformatics toolkit for exploring disease associations and ncRNA function. Bioinformatics 2018; 34(11): 1953-6.
[http://dx.doi.org/10.1093/bioinformatics/bty002] [PMID: 29365045]
Swamidass SJ. Mining small-molecule screens to repurpose drugs. Brief Bioinform 2011; 12(4): 327-35.
[PMID: 21715466]
Pihan E, Colliandre L, Guichou J-F, Douguet D. e-Drug3D: 3D structure collections dedicated to drug repurposing and fragment-based drug design. Bioinformatics 2012; 28(11): 1540-1.
[http://dx.doi.org/10.1093/bioinformatics/bts186] [PMID: 22539672]
Novick PA, Ortiz OF, Poelman J, Abdulhay AY, Pande VS. SWEETLEAD: an in silico database of approved drugs, regulated chemicals, and herbal isolates for computer-aided drug discovery. PLoS One 2013; 8(11)e79568
[http://dx.doi.org/10.1371/journal.pone.0079568] [PMID: 24223973]
Yang CC, Zhao M. Mining heterogeneous network for drug repositioning using phenotypic information extracted from social media and pharmaceutical databases. Artif Intell Med 2019; 96: 80-92.
[http://dx.doi.org/10.1016/j.artmed.2019.03.003] [PMID: 31164213]
Gottlieb A, Stein GY, Ruppin E, Sharan R. PREDICT: a method for inferring novel drug indications with application to personalized medicine. Mol Syst Biol 2011; 7: 496-6.
[http://dx.doi.org/10.1038/msb.2011.26] [PMID: 21654673]
Luo H, Wang J, Li M, et al. Drug repositioning based on comprehensive similarity measures and Bi-Random Walk algorithm 322016: 2664.
Zhang J, Li C, Lin Y, Shao Y, Li S. Computational drug repositioning using collaborative filtering via multi-source fusion. Expert Syst Appl 2017; 84: 281-9.
Campillos M, Kuhn M, Gavin A-C, Jensen LJ, Bork P. Drug target identification using side-effect similarity. Science 2008; 321(5886): 263-6.
[http://dx.doi.org/10.1126/science.1158140] [PMID: 18621671]
Kim E, Choi AS, Nam H. Drug repositioning of herbal compounds via a machine-learning approach. BMC Bioinformatics 2019; 20(Suppl. 10): 247.
[http://dx.doi.org/10.1186/s12859-019-2811-8] [PMID: 31138103]
Lotfi Shahreza M, Ghadiri N, Mousavi SR, Varshosaz J, Green JR. Heter-LP: A heterogeneous label propagation algorithm and its application in drug repositioning. J Biomed Inform 2017; 68: 167-83.
[http://dx.doi.org/10.1016/j.jbi.2017.03.006] [PMID: 28300647]
Suthram S, Dudley JT, Chiang AP, Chen R, Hastie TJ, Butte AJ. Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets. PLOS Comput Biol 2010; 6(2)e1000662
[http://dx.doi.org/10.1371/journal.pcbi.1000662] [PMID: 20140234]
Hu Y, Zhao L, Liu Z, et al. DisSetSim: an online system for calculating similarity between disease sets. J Biomed Semantics 2017; 8(Suppl. 1): 28.
[http://dx.doi.org/10.1186/s13326-017-0140-2] [PMID: 29297411]
Cheng L, Sun J, Xu W, Dong L, Hu Y, Zhou M. OAHG: an integrated resource for annotating human genes with multi-level ontologies. Sci Rep 2016; 6: 34820.
[http://dx.doi.org/10.1038/srep34820] [PMID: 27703231]
Cheng L, Jiang Y, Wang Z, et al. DisSim: an online system for exploring significant similar diseases and exhibiting potential therapeutic drugs. Sci Rep 2016; 6: 30024.
[http://dx.doi.org/10.1038/srep30024] [PMID: 27457921]
van Driel MA, Bruggeman J, Vriend G, Brunner HG, Leunissen JAM. A text-mining analysis of the human phenome. Eur J Hum Genet 2006; 14(5): 535-42.
[http://dx.doi.org/10.1038/sj.ejhg.5201585] [PMID: 16493445]
Manchanda S, Anand A. Representation Learning of Drug and Disease Terms for Drug Repositioning
Wang YY, Cui C, Qi L, Yan H, Zhao XM. DrPOCS: Drug Repositioning based on projection onto convex sets. IEEE/ACM Trans Comput Biol Bioinformatics 2019; 16(1): 154-62.
[http://dx.doi.org/10.1109/TCBB.2018.2830384] [PMID: 29993698]
Wang D, Wang J, Lu M, Song F, Cui Q. Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics 2010; 26(13): 1644-50.
[http://dx.doi.org/10.1093/bioinformatics/btq241] [PMID: 20439255]
Liang X, Zhang P, Yan L, et al. LRSSL: predict and interpret drug-disease associations based on data integration using sparse subspace learning. Bioinformatics 2017; 33(8): 1187-96.
[http://dx.doi.org/10.1093/bioinformatics/btw770] [PMID: 28096083]
Chen H, Li J. A flexible and robust multi-source learning algorithm for drug repositioning. Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. 510-.
Wang R, Li S, Wong MH, Leung KS. Drug-protein-disease association prediction and drug repositioning based on tensor decomposition. IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
Yu L, Zhao J, Gao L. Drug repositioning based on triangularly balanced structure for tissue-specific diseases in incomplete interactome. Artif Intell Med 2017; 77: 53-63.
[http://dx.doi.org/10.1016/j.artmed.2017.03.009] [PMID: 28545612]
Chen H, Zhang Z. Prediction of drug-disease associations for drug repositioning through drug-miRNA-disease heterogeneous networkEEE Access 2018; 6: 45281-7
Zhang W, Yue X, Chen Y, et al. Predicting drug-disease associations based on the known association bipartite network.2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 503-9.
Hoehndorf R, Oellrich A, Rebholz-Schuhmann D, Schofield PN, Gkoutos GV. Linking PharmGKB to phenotype studies and animal models of disease for drug repurposing. Pac Symp Biocomput 2012; 17: 388-99.
[PMID: 22174294]
Jahchan NS, Dudley JT, Mazur PK, et al. A drug repositioning approach identifies tricyclic antidepressants as inhibitors of small cell lung cancer and other neuroendocrine tumors. Cancer Discov 2013; 3(12): 1364-77.
[http://dx.doi.org/10.1158/2159-8290.CD-13-0183] [PMID: 24078773]
Cheng L, Wang P, Tian R, et al. LncRNA2Target v2.0: a comprehensive database for target genes of lncRNAs in human and mouse. Nucleic Acids Res 2019; 47(D1): D140-4.
[http://dx.doi.org/10.1093/nar/gky1051] [PMID: 30380072]
Cheng L, Hu Y. Human disease system biology. Curr Gene Ther 2018; 18(5): 255-6.
[http://dx.doi.org/10.2174/1566523218666181010101114] [PMID: 30306867]
Jiang W, Chen X, Liao M, et al. Identification of links between small molecules and miRNAs in human cancers based on transcriptional responses. Sci Rep 2012; 2: 282.
[http://dx.doi.org/10.1038/srep00282] [PMID: 22355792]
Liu Z, Borlak J, Tong W. Deciphering miRNA transcription factor feed-forward loops to identify drug repurposing candidates for cystic fibrosis. Genome Med 2014; 6(12): 94-4.
[http://dx.doi.org/10.1186/s13073-014-0094-2] [PMID: 25484921]
Tang H, Chen W, Lin H. Identification of immunoglobulins using Chou’s pseudo amino acid composition with feature selection technique. Mol Biosyst 2016; 12(4): 1269-75.
[http://dx.doi.org/10.1039/C5MB00883B] [PMID: 26883492]
Chen XX, Tang H, Li WC, et al. Identification of bacterial cell wall lyases via pseudo amino acid composition. BioMed Res Int 2016; 20161654623
[http://dx.doi.org/10.1155/2016/1654623] [PMID: 27437396]
Wang Y, Chen S, Deng N, Wang Y. Drug repositioning by kernel-based integration of molecular structure, molecular activity, and phenotype data. PLoS One 2013; 8(11)e78518
[http://dx.doi.org/10.1371/journal.pone.0078518] [PMID: 24244318]
Yang W, Zhu XJ, Huang J, Ding H, Lin H. A brief survey of machine learning methods in protein sub-Golgi localization. Curr Bioinform 2019; 14: 234-40.
Tan JX, Lv H, Wang F, Dao FY, Chen W, Ding H. A survey for predicting enzyme family classes using machine learning methods. Curr Drug Targets 2019; 20(5): 540-50.
[http://dx.doi.org/10.2174/1389450119666181002143355] [PMID: 30277150]
Tang H, Zhao YW, Zou P, et al. HBPred: a tool to identify growth hormone-binding proteins. Int J Biol Sci 2018; 14(8): 957-64.
[http://dx.doi.org/10.7150/ijbs.24174] [PMID: 29989085]
Yang H, Tang H, Chen XX, et al. Identification of secretory proteins in Mycobacterium tuberculosis using pseudo amino acid composition. BioMed Res Int 2016; 20165413903
[http://dx.doi.org/10.1155/2016/5413903] [PMID: 27597968]
Ozsoy MG, Özyer T, Polat F, Alhajj R. Realizing drug repositioning by adapting a recommendation system to handle the process. BMC Bioinformatics 2018; 19(1): 136.
[http://dx.doi.org/10.1186/s12859-018-2142-1] [PMID: 29649971]
Yang J, Li Z, Fan X, Cheng Y. Drug-disease association and drug-repositioning predictions in complex diseases using causal inference-probabilistic matrix factorization. J Chem Inf Model 2014; 54(9): 2562-9.
[http://dx.doi.org/10.1021/ci500340n] [PMID: 25116798]
Wei L, Su R, Wang B, Li X, Zou Q, Gao X. Integration of deep feature representations and handcrafted features to improve the prediction of N 6-methyladenosine sites. Neurocomputing 2019; 324: 3-9.
Wei L, Su R, Luan S, et al. Iterative feature representations improve N4-methylcytosine site prediction. Bioinformatics 2019; 35(23): 4930-7.
[http://dx.doi.org/10.1093/bioinformatics/btz408] [PMID: 31099381]
Ru X, Cao P, Li L, Zou Q. Selecting essential micrornas using a novel voting method. Mol Ther Nucleic Acids 2019; 18: 16-23.
[http://dx.doi.org/10.1016/j.omtn.2019.07.019] [PMID: 31479921]
Wan S, Duan Y, Zou Q. HPSLPred: An ensemble multi-label classifier for human protein subcellular location prediction with imbalanced source. Proteomics 2017; 17(17-18)1700262
[http://dx.doi.org/10.1002/pmic.201700262] [PMID: 28776938]
Lin C, Zou Y, Qin J, et al. Hierarchical classification of protein folds using a novel ensemble classifier. PLoS One 2013; 8(2)e56499
[http://dx.doi.org/10.1371/journal.pone.0056499] [PMID: 23437146]
Wu G, Liu J, Yue X. Prediction of drug-disease associations based on ensemble meta paths and singular value decomposition. BMC Bioinformatics 2019; 20(Suppl. 3): 134.
[http://dx.doi.org/10.1186/s12859-019-2644-5] [PMID: 30925858]
Di Y-Z, Chen P, Zheng C-H. Similarity-based integrated method for predicting drug-disease interactionsintelligent computing theories and application. Cham: Springer International Publishing 2018; pp. 395-400.
Liu X, Hong Z, Liu J, et al. Computational methods for identifying the critical nodes in biological networks. Brief Bioinform 2019; (2): 486-97.
[http://dx.doi.org/10.1093/bib/bbz011] [PMID: 30753282]
Zhu L, Su F, Xu Y, Zou Q. 2018 Network-based method for mining novel HPV infection related genes using random walk with restart algorithm. Biochem Biophys Acta Mol Basis Dis 2018; 1864: 2376-83.
Zhang P, Wang F, Hu J. Towards drug repositioning: a unified computational framework for integrating multiple aspects of drug similarity and disease similarity. AMIA Annu Symp Proc 2014; 2014: 1258-67.
[PMID: 25954437]
Napolitano F, Zhao Y, Moreira VM, et al. Drug repositioning: a machine-learning approach through data integration. J Cheminform 2013; 5(1): 30.
[http://dx.doi.org/10.1186/1758-2946-5-30] [PMID: 23800010]
Li J, Lu Z. A New Method for computational drug repositioning using drug pairwise similarity. Proceedings IEEE Int Conf Bioinformatics Biomed 2012; 2012: 1-4.
[http://dx.doi.org/10.1109/BIBM.2012.6392722] [PMID: 25264495]
Wu C, Gudivada RC, Aronow BJ, Jegga AG. Computational drug repositioning through heterogeneous network clustering. BMC Syst Biol 2013; 7(Suppl. 5): S6-6.
[http://dx.doi.org/10.1186/1752-0509-7-S5-S6] [PMID: 24564976]
Wang W, Yang S, Zhang X, Li J. Drug repositioning by integrating target information through a heterogeneous network model. Bioinformatics 2014; 30(20): 2923-30.
[http://dx.doi.org/10.1093/bioinformatics/btu403] [PMID: 24974205]
Lee T, Yoon Y. Drug repositioning using drug-disease vectors based on an integrated network. BMC Bioinformatics 2018; 19(1): 446.
[http://dx.doi.org/10.1186/s12859-018-2490-x] [PMID: 30463505]
Li J, Lu Z. Pathway-based drug repositioning using causal inference. BMC Bioinformatics 2013; 14(Suppl. 16): S3.
[http://dx.doi.org/10.1186/1471-2105-14-S16-S3] [PMID: 24564553]
Chen H, Zhang H, Zhang Z, Cao Y, Tang W. Network-based inference methods for drug repositioning. Comput Math Methods Med 2015; 2015130620
[http://dx.doi.org/10.1155/2015/130620] [PMID: 25969690]
Martínez V, Navarro C, Cano C, Fajardo W, Blanco A. DrugNet: network-based drug-disease prioritization by integrating heterogeneous data. Artif Intell Med 2015; 63(1): 41-9.
[http://dx.doi.org/10.1016/j.artmed.2014.11.003] [PMID: 25704113]
Liu H, Song Y, Guan J, Luo L, Zhuang Z. Inferring new indications for approved drugs via random walk on drug-disease heterogenous networks. BMC Bioinformatics 2016; 17(Suppl. 17): 539-9.
[http://dx.doi.org/10.1186/s12859-016-1336-7] [PMID: 28155639]
Fukuoka Y, Takei D, Ogawa H. A two-step drug repositioning method based on a protein-protein interaction network of genes shared by two diseases and the similarity of drugs. Bioinformation 2013; 9(2): 89-93.
[http://dx.doi.org/10.6026/97320630009089] [PMID: 23390352]
Tan F, Yang R, Xu X, et al. Drug repositioning by applying ‘expression profiles’ generated by integrating chemical structure similarity and gene semantic similarity. Mol Biosyst 2014; 10(5): 1126-38.
[http://dx.doi.org/10.1039/c3mb70554d] [PMID: 24603772]
Ng C, Hauptman R, Zhang Y, Bourne PE, Xie L. Anti-infectious drug repurposing using an integrated chemical genomics and structural systems biology approach. Pac Symp Biocomput 2014; 19: 136-47.
[PMID: 24297541]
Cheng L, Zhuang H, Ju H, et al. Exposing the causal effect of body mass index on the risk of type 2 diabetes mellitus: a mendelian randomization study. Front Genet 2019; 10: 94.
[http://dx.doi.org/10.3389/fgene.2019.00094] [PMID: 30891058]
Cheng L, Zhuang H, Yang S, Jiang H, Wang S, Zhang J. Exposing the causal effect of c-reactive protein on the risk of type 2 diabetes mellitus: A mendelian randomization study. Front Genet 2018; 9: 657.
[http://dx.doi.org/10.3389/fgene.2018.00657] [PMID: 30619477]
Zhu Q, Luo J, Ding P, Xiao Q. GRTR: Drug-disease association prediction based on graph regularized transductive regression on heterogeneous network. 14th International Symposium on Bioinformatics Research and Applications. 13-25.
Andronis C, Sharma A, Virvilis V, Deftereos S, Persidis A. Literature mining, ontologies and information visualization for drug repurposing. Brief Bioinform 2011; 12(4): 357-68.
[http://dx.doi.org/10.1093/bib/bbr005] [PMID: 21712342]
Tari LB, Patel JH. Systematic drug repurposing through text mining. Methods Mol Biol 2014; 1159: 253-67.
[http://dx.doi.org/10.1007/978-1-4939-0709-0_14] [PMID: 24788271]
Li J, Lu Z. Systematic identification of pharmacogenomics information from clinical trials. J Biomed Inform 2012; 45(5): 870-8.
[http://dx.doi.org/10.1016/j.jbi.2012.04.005] [PMID: 22546622]
Arighi CN, Wu CH, Cohen KB, et al. BioCreative-IV virtual issue. Database (Oxford) 2014; 2014bau039
[http://dx.doi.org/10.1093/database/bau039] [PMID: 24852177]
Swanson DR. Migraine and magnesium: eleven neglected connections. Perspect Biol Med 1988; 31(4): 526-57.
[http://dx.doi.org/10.1353/pbm.1988.0009] [PMID: 3075738]
Rastegar-Mojarad M, Elayavilli RK, Li D, Prasad R, Liu H. A new method for prioritizing drug repositioning candidates extracted by literature-based discovery. IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 669-74.
Wu G, Liu J, Wang C. Predicting drug-disease interactions by semi-supervised graph cut algorithm and three-layer data integration. BMC Med Genomics 2017; 10(Suppl. 5): 79.
[http://dx.doi.org/10.1186/s12920-017-0311-0] [PMID: 29297383]
Zhu Q, Tao CUI, Shen F, Chute CG. Exploring the pharmacogenomics knowledge base (pharmgkb) for repositioning breast cancer drugs by leveraging web ontology language (owl) and cheminformatics approaches Biocomputing 2014. WORLD SCIENTIFIC 2013; pp. 172-82.
Chen B, Ding Y, Wild DJ. Assessing drug target association using semantic linked data. PLOS Comput Biol 2012; 8(7)e1002574
[http://dx.doi.org/10.1371/journal.pcbi.1002574] [PMID: 22859915]
Xiaojin Z, Andrew G. Introduction to Semi-Supervised Learning. Morgan & Claypool 2009.
Zhao ZQ, Glotin H, Gao J, Wu XD. Multi-classes semi-supervised learning on riemannian manifolds. International Conference on Computational Intelligence & Natural Computing.
Iskar M, Campillos M, Kuhn M, Jensen LJ, van Noort V, Bork P. Drug-induced regulation of target expression. PLOS Comput Biol 2010; 6(9)1000925
[http://dx.doi.org/10.1371/journal.pcbi.1000925] [PMID: 20838579]
Cockell SJ, Weile J, Lord P, et al. An integrated dataset for in silico drug discovery. J Integr Bioinform 2010; 7(3): 7.
[http://dx.doi.org/10.1515/jib-2010-116] [PMID: 20375448]
Zhang J, Zhang Z, Chen Z, Deng L. Integrating multiple heterogeneous networks for novel lncrna-disease association inference. IEEE/ACM Trans Comput Biol Bioinformatics 2019; 16(2): 396-406.
[http://dx.doi.org/10.1109/TCBB.2017.2701379] [PMID: 28489543]
Zhang Z, Zhang J, Fan C, Tang Y, Deng L. KATZLGO: Large-scale prediction of lncrna functions by using the katz measure based on multiple networks. IEEE/ACM Trans Comput Biol Bioinformatics 2019; 16(2): 407-16.
[http://dx.doi.org/10.1109/TCBB.2017.2704587] [PMID: 28534780]
Deng L, Wang J, Zhang J. Predicting gene ontology function of human micrornas by integrating multiple networks. Front Genet 2019; 10: 3.
[http://dx.doi.org/10.3389/fgene.2019.00003] [PMID: 30761178]
Nie L, Deng L, Fan C, Zhan W, Tang Y. Prediction of protein S-sulfenylation sites using a deep belief network. Curr Bioinform 2018; 13: 461-7.
Peng J, Wang X, Shang X. Combining gene ontology with deep neural networks to enhance the clustering of single cell RNA-Seq data. BMC Bioinformatics 2019; 20(Suppl. 8): 284.
[http://dx.doi.org/10.1186/s12859-019-2769-6] [PMID: 31182005]
Peng J, Guan J, Shang X. Predicting Parkinson’s disease genes based on Node2vec and autoencoder. Front Genet 2019; 10: 226.
[http://dx.doi.org/10.3389/fgene.2019.00226] [PMID: 31001311]
Peng J, Hui W, Li Q, et al. A learning-based framework for miRNA-disease association identification using neural networks. Bioinformatics 2019; 35(21): 4364-71.
[http://dx.doi.org/10.1093/bioinformatics/btz254] [PMID: 30977780]
Tang Y, Liu D, Wang Z, Wen T, Deng L. A boosting approach for prediction of protein-RNA binding residues. BMC Bioinformatics 2017; 18(Suppl. 13): 465.
[http://dx.doi.org/10.1186/s12859-017-1879-2] [PMID: 29219069]
Deng L, Sui Y, Zhang J. XGBPRH: Prediction of binding hot spots at protein–rna interfaces utilizing extreme gradient boosting. Genes (Basel) 2019; 10(3): 242.

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Year: 2020
Published on: 11 August, 2020
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DOI: 10.2174/1381612826666200116145559
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