{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:07:49Z","timestamp":1783436869332,"version":"3.54.6"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"22-23","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972423"],"award-info":[{"award-number":["61972423"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Graduate Research Innovation Project of Hunan","award":["CX20190125"],"award-info":[{"award-number":["CX20190125"]}]},{"name":"Hunan Provincial Science and technology Program","award":["2018wk4001"],"award-info":[{"award-number":["2018wk4001"]}]},{"name":"111Project","award":["B18059"],"award-info":[{"award-number":["B18059"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Emerging evidence presents that traditional drug discovery experiment is time-consuming and high costs. Computational drug repositioning plays a critical role in saving time and resources for drug research and discovery. Therefore, developing more accurate and efficient approaches is imperative. Heterogeneous graph inference is a classical method in computational drug repositioning, which not only has high convergence precision, but also has fast convergence speed. However, the method has not fully considered the sparsity of heterogeneous association network. In addition, rough similarity measure can reduce the performance in identifying drug-associated indications.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this article, we propose a heterogeneous graph inference with matrix completion (HGIMC) method to predict potential indications for approved and novel drugs. First, we use a bounded matrix completion (BMC) model to prefill a part of the missing entries in original drug\u2013disease association matrix. This step can add more positive and formative drug\u2013disease edges between drug network and disease network. Second, Gaussian radial basis function (GRB) is employed to improve the drug and disease similarities since the performance of heterogeneous graph inference more relies on similarity measures. Next, based on the updated drug\u2013disease associations and new similarity measures of drug and disease, we construct a novel heterogeneous drug\u2013disease network. Finally, HGIMC utilizes the heterogeneous network to infer the scores of unknown association pairs, and then recommend the promising indications for drugs. To evaluate the performance of our method, HGIMC is compared with five state-of-the-art approaches of drug repositioning in the 10-fold cross-validation and de novo tests. As the numerical results shown, HGIMC not only achieves a better prediction performance but also has an excellent computation efficiency. In addition, cases studies also confirm the effectiveness of our method in practical application.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availabilityand implementation<\/jats:title>\n                  <jats:p>The HGIMC software and data are freely available at https:\/\/github.com\/BioinformaticsCSU\/HGIMC, https:\/\/hub.docker.com\/repository\/docker\/yangmy84\/hgimc and http:\/\/doi.org\/10.5281\/zenodo.4285640.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa1024","type":"journal-article","created":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T12:45:08Z","timestamp":1606394708000},"page":"5456-5464","source":"Crossref","is-referenced-by-count":32,"title":["Heterogeneous graph inference with matrix completion for computational drug repositioning"],"prefix":"10.1093","volume":"36","author":[{"given":"Mengyun","family":"Yang","sequence":"first","affiliation":[{"name":"The Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"},{"name":"School of Science, Shaoyang University , Shaoyang 422000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lan","family":"Huang","sequence":"additional","affiliation":[{"name":"The Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunpei","family":"Xu","sequence":"additional","affiliation":[{"name":"The Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9201-6912","authenticated-orcid":false,"given":"Chengqian","family":"Lu","sequence":"additional","affiliation":[{"name":"The Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1516-0480","authenticated-orcid":false,"given":"Jianxin","family":"Wang","sequence":"additional","affiliation":[{"name":"The Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering, Central South University , Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,12,17]]},"reference":[{"key":"2023062707303538100_btaa1024-B1","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1093\/nar\/30.1.52","article-title":"Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders","volume":"30","author":"Ada","year":"2002","journal-title":"Nucleic Acids Res"},{"key":"2023062707303538100_btaa1024-B2","doi-asserted-by":"crossref","first-page":"2524","DOI":"10.1021\/acs.molpharmaceut.6b00248","article-title":"Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data","volume":"13","author":"Aliper","year":"2016","journal-title":"Mol. Pharmaceutics"},{"key":"2023062707303538100_btaa1024-B3","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/nrg2918","article-title":"Network medicine: a network-based approach to human disease","volume":"12","author":"Barab\u00e1si","year":"2011","journal-title":"Nat. Rev. Genet"},{"key":"2023062707303538100_btaa1024-B4","doi-asserted-by":"crossref","first-page":"1956","DOI":"10.1137\/080738970","article-title":"A singular value thresholding algorithm for matrix completion","volume":"20","author":"Cai","year":"2010","journal-title":"SIAM J. Optim"},{"key":"2023062707303538100_btaa1024-B5","doi-asserted-by":"crossref","first-page":"e1004975","DOI":"10.1371\/journal.pcbi.1004975","article-title":"NLLSS: predicting synergistic drug combinations based on semi-supervised learning","volume":"12","author":"Chen","year":"2016","journal-title":"PLoS Comput. Biol"},{"key":"2023062707303538100_btaa1024-B6","doi-asserted-by":"crossref","first-page":"4256","DOI":"10.1093\/bioinformatics\/bty503","article-title":"Predicting miRNA-disease association based on inductive matrix completion","volume":"34","author":"Chen","year":"2018","journal-title":"Bioinformatics"},{"key":"2023062707303538100_btaa1024-B7","doi-asserted-by":"crossref","first-page":"e1006418","DOI":"10.1371\/journal.pcbi.1006418","article-title":"MDHGI: matrix decomposition and heterogeneous graph inference for miRNA-disease association prediction","volume":"14","author":"Chen","year":"2018","journal-title":"PLoS Comput. Biol"},{"key":"2023062707303538100_btaa1024-B8","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1038\/clpt.2009.103","article-title":"Systematic evaluation of drug\u2013disease relationships to identify leads for novel drug uses","volume":"86","author":"Chiang","year":"2009","journal-title":"Clin. Pharmacol. Therap"},{"key":"2023062707303538100_btaa1024-B9","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1038\/448645a","article-title":"New uses for old drugs","volume":"448","author":"Chong","year":"2007","journal-title":"Nature"},{"key":"2023062707303538100_btaa1024-B10","doi-asserted-by":"crossref","first-page":"D1104","DOI":"10.1093\/nar\/gks994","article-title":"The comparative toxicogenomics database: update 2013","volume":"41","author":"Davis","year":"2013","journal-title":"Nucleic Acids Res"},{"key":"2023062707303538100_btaa1024-B11","doi-asserted-by":"crossref","first-page":"11634","DOI":"10.1039\/C9RA11043G","article-title":"Heterogeneous graph inference based on similarity network fusion for predicting lncRNA-miRNA interaction","volume":"10","author":"Fan","year":"2020","journal-title":"RSC Advances"},{"key":"2023062707303538100_btaa1024-B12","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1038\/msb.2011.26","article-title":"PREDICT: a method for inferring novel drug indications with application to personalized medicine","volume":"7","author":"Gottlieb","year":"2011","journal-title":"Mol. Syst. Biol"},{"key":"2023062707303538100_btaa1024-B13","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa265","article-title":"Drug\u2013drug similarity measure and its applications","author":"Huang","year":"2020","journal-title":"Brief. Bioinform"},{"key":"2023062707303538100_btaa1024-B14","first-page":"223","article-title":"Nouvelles recheres sur la distribution florale","volume":"44","author":"Jaccard","year":"1908","journal-title":"Bull. Soc. Vaud. Sci. Nat"},{"key":"2023062707303538100_btaa1024-B15","doi-asserted-by":"crossref","first-page":"5389","DOI":"10.2174\/0929867325666180530100332","article-title":"Computational drug repurposing: current trends","volume":"26","author":"Karaman","year":"2019","journal-title":"Curr. Med. Chem"},{"key":"2023062707303538100_btaa1024-B16","article-title":"Anticancer drug synergy prediction in understudied tissues using transfer learning","author":"Kim","year":"2020","journal-title":"J. Am. Med. Inf. Assoc"},{"key":"2023062707303538100_btaa1024-B17","doi-asserted-by":"crossref","first-page":"D1075","DOI":"10.1093\/nar\/gkv1075","article-title":"The SIDER database of drugs and side effects","volume":"44","author":"Kuhn","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2023062707303538100_btaa1024-B18","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1093\/bib\/bbv020","article-title":"A survey of current trends in computational drug repositioning","volume":"17","author":"Li","year":"2016","journal-title":"Brief. Bioinform"},{"key":"2023062707303538100_btaa1024-B19","doi-asserted-by":"crossref","first-page":"2664","DOI":"10.1093\/bioinformatics\/btw228","article-title":"Drug repositioning based on comprehensive similarity measures and Bi-random walk algorithm","volume":"32","author":"Luo","year":"2016","journal-title":"Bioinformatics"},{"key":"2023062707303538100_btaa1024-B20","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1093\/bioinformatics\/bty013","article-title":"Computational drug repositioning using low-rank matrix approximation and randomized algorithms","volume":"34","author":"Luo","year":"2018","journal-title":"Bioinformatics"},{"key":"2023062707303538100_btaa1024-B21","article-title":"Biomedical data and computational models for drug repositioning: a comprehensive review","author":"Luo","year":"2020","journal-title":"Brief. Bioinform"},{"key":"2023062707303538100_btaa1024-B22","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s10107-009-0306-5","article-title":"Fixed point and Bregman iterative methods for matrix rank minimization","volume":"128","author":"Ma","year":"2011","journal-title":"Math. Program"},{"key":"2023062707303538100_btaa1024-B23","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.artmed.2014.11.003","article-title":"DrugNet: network-based drug\u2013disease prioritization by integrating heterogeneous data","volume":"63","author":"Martinez","year":"2015","journal-title":"Artif. Intell. Med"},{"key":"2023062707303538100_btaa1024-B24","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1186\/1758-2946-5-30","article-title":"Drug repositioning: a machine-learning approach through data integration","volume":"5","author":"Napolitano","year":"2013","journal-title":"J. Cheminf"},{"key":"2023062707303538100_btaa1024-B25","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/nrd.2018.168","article-title":"Drug repurposing: progress, challenges and recommendations","volume":"18","author":"Pushpakom","year":"2019","journal-title":"Nat. Rev. Drug Discov"},{"key":"2023062707303538100_btaa1024-B26","doi-asserted-by":"crossref","first-page":"308","DOI":"10.26599\/BDMA.2018.9020008","article-title":"A survey of matrix completion methods for recommendation systems","volume":"1","author":"Ramlatchan","year":"2018","journal-title":"Big Data Mining Anal"},{"key":"2023062707303538100_btaa1024-B27","first-page":"448","author":"Resnik","year":"1995"},{"key":"2023062707303538100_btaa1024-B28","first-page":"493","article-title":"The Chemistry Development Kit (CDK): An open-source java library for chemo-and bioinformatics","volume":"34","author":"Steinbeck","year":"2003","journal-title":"Cheminformatics"},{"key":"2023062707303538100_btaa1024-B29","article-title":"Exploration of databases and methods supporting drug repurposing: a comprehensive survey","author":"Tanoli","year":"2020","journal-title":"Brief. Bioinform"},{"key":"2023062707303538100_btaa1024-B30","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1038\/sj.ejhg.5201585","article-title":"A text-mining analysis of the human phenome","volume":"14","author":"Van","year":"2006","journal-title":"Eur. J. Hum. Genet"},{"key":"2023062707303538100_btaa1024-B31","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1093\/bioinformatics\/btm087","article-title":"A new method to measure the semantic similarity of GO terms","volume":"23","author":"Wang","year":"2007","journal-title":"Bioinformatics"},{"key":"2023062707303538100_btaa1024-B32","first-page":"53","article-title":"Drug target predictions based on heterogeneous graph inference","volume":"18","author":"Wang","year":"2013","journal-title":"Pac. Symp. Biocomput"},{"key":"2023062707303538100_btaa1024-B33","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1038\/nmeth.2810","article-title":"Similarity network fusion for aggregating data types on a genomic scale","volume":"11","author":"Wang","year":"2014","journal-title":"Nat. Methods"},{"key":"2023062707303538100_btaa1024-B34","doi-asserted-by":"crossref","first-page":"2923","DOI":"10.1093\/bioinformatics\/btu403","article-title":"Drug repositioning by integrating target information through a heterogeneous network model","volume":"30","author":"Wang","year":"2014","journal-title":"Bioinformatics"},{"key":"2023062707303538100_btaa1024-B35","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1021\/ci00057a005","article-title":"SMILES, a chemical language and information system. 1. introduction to methodology and encoding rules","volume":"28","author":"Weininger","year":"1988","journal-title":"J. Chem. Inf. Comput. Sci"},{"key":"2023062707303538100_btaa1024-B36","doi-asserted-by":"crossref","first-page":"D668","DOI":"10.1093\/nar\/gkj067","article-title":"DrugBank: a comprehensive resource for in silico drug discovery and exploration","volume":"34","author":"Wishart","year":"2006","journal-title":"Nucleic Acids Res"},{"key":"2023062707303538100_btaa1024-B37","doi-asserted-by":"crossref","first-page":"4108","DOI":"10.1093\/bioinformatics\/btz182","article-title":"Drug repositioning through integration of prior knowledge and projections of drugs and diseases","volume":"35","author":"Xuan","year":"2019","journal-title":"Bioinformatics"},{"key":"2023062707303538100_btaa1024-B38","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1090\/S0025-5718-2012-02598-1","article-title":"Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization","volume":"82","author":"Yang","year":"2012","journal-title":"Math. Comput"},{"key":"2023062707303538100_btaa1024-B39","doi-asserted-by":"crossref","first-page":"e28025","DOI":"10.1371\/journal.pone.0028025","article-title":"Systematic drug repositioning based on clinical side-effects","volume":"6","author":"Yang","year":"2011","journal-title":"PLoS One"},{"key":"2023062707303538100_btaa1024-B40","doi-asserted-by":"crossref","first-page":"i455","DOI":"10.1093\/bioinformatics\/btz331","article-title":"Drug repositioning based on bounded nuclear norm regularization","volume":"35","author":"Yang","year":"2019","journal-title":"Bioinformatics (ISMB\/ECCB 2019)"},{"key":"2023062707303538100_btaa1024-B41","doi-asserted-by":"crossref","first-page":"e1007541","DOI":"10.1371\/journal.pcbi.1007541","article-title":"Overlap matrix completion for predicting drug-associated indications","volume":"15","author":"Yang","year":"2019","journal-title":"PLoS Comput. Biol"},{"key":"2023062707303538100_btaa1024-B42","article-title":"Feature and nuclear norm minimization for matrix completion","author":"Yang","year":"2020","journal-title":"IEEE Trans. Knowl. Data Eng"},{"key":"2023062707303538100_btaa1024-B43","doi-asserted-by":"crossref","first-page":"W43","DOI":"10.1093\/nar\/gkz337","article-title":"DrugComb: an integrative cancer drug combination data portal","volume":"47","author":"Zagidullin","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023062707303538100_btaa1024-B44","doi-asserted-by":"crossref","first-page":"5191","DOI":"10.1093\/bioinformatics\/btz418","article-title":"deepDR: a network-based deep learning approach to in silico drug repositioning","volume":"35","author":"Zeng","year":"2019","journal-title":"Bioinformatics"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btaa1024\/35176647\/btaa1024.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/22-23\/5456\/50716706\/btaa1024.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/22-23\/5456\/50716706\/btaa1024.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T08:00:37Z","timestamp":1687852837000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/36\/22-23\/5456\/6040748"}},"subtitle":[],"editor":[{"given":"Anthony","family":"Mathelier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2020,12,1]]},"references-count":44,"journal-issue":{"issue":"22-23","published-print":{"date-parts":[[2021,4,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaa1024","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,12,1]]},"published":{"date-parts":[[2020,12,1]]}}}