{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T11:19:51Z","timestamp":1761218391605,"version":"3.37.3"},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"23","license":[{"start":{"date-parts":[[2021,6,27]],"date-time":"2021-06-27T00:00:00Z","timestamp":1624752000000},"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":["61873202"],"award-info":[{"award-number":["61873202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The driver genes play a key role in the evolutionary process of cancer. Effectively identifying these driver genes is crucial to cancer diagnosis and treatment. However, due to the high heterogeneity of cancers, it remains challenging to identify the driver genes for individual patients. Although some computational methods have been proposed to tackle this problem, they seldom consider the fact that the genes functionally similar to the well-established driver genes may likely play similar roles in cancer process, which potentially promotes the driver gene identification. Thus, here we developed a novel approach of IMCDriver to promote the driver gene identification both for cohorts and individual patients.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>IMCDriver first considers the well-established driver genes as prior information, and adopts the using multi-omics data (e.g. somatic mutation, gene expression and protein\u2013protein interaction) to compute the similarity between patients\/genes. Then, IMCDriver prioritizes the personalized mutated genes according to their functional similarity to the well-established driver genes via Inductive Matrix Completion. Finally, IMCDriver identifies the highly rank-ordered genes as the personalized driver genes. The results on five cancer datasets from the Cancer Genome Consortium show that our IMCDriver outperforms other existing state-of-the-art methods both in the cohort and patient-specific driver gene identification. IMCDriver also reveals some novel driver genes that potentially drive cancer development. In addition, even for the driver genes rarely mutated among a population, IMCDriver can still identify them and prioritize them with high priorities.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Code available at https:\/\/github.com\/NWPU-903PR\/IMCDriver.<\/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\/btab477","type":"journal-article","created":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T19:13:34Z","timestamp":1624648414000},"page":"4477-4484","source":"Crossref","is-referenced-by-count":23,"title":["Identifying driver genes for individual patients through inductive matrix completion"],"prefix":"10.1093","volume":"37","author":[{"given":"Tong","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University , Xi\u2019an 710072, China"},{"name":"School of Electrical and Mechanical Engineering, Pingdingshan University , Pingdingshan 467000, China"}]},{"given":"Shao-Wu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University , Xi\u2019an 710072, China"}]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University , Xi\u2019an 710072, China"}]}],"member":"286","published-online":{"date-parts":[[2021,6,27]]},"reference":[{"key":"2023061310492327800_btab477-B1","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1210\/js.2018-00106","article-title":"Pappa2 as a therapeutic modulator of igf-i bioavailability: in vivo and in vitro evidence","volume":"2","author":"Andrew","year":"2018","journal-title":"J. Endocr. Soc"},{"key":"2023061310492327800_btab477-B2","doi-asserted-by":"crossref","first-page":"R124","DOI":"10.1186\/gb-2012-13-12-r124","article-title":"Drivernet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer","volume":"13","author":"Bashashati","year":"2012","journal-title":"Genome Biol"},{"key":"2023061310492327800_btab477-B3","doi-asserted-by":"crossref","first-page":"e44","DOI":"10.1093\/nar\/gku1393","article-title":"Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles","volume":"43","author":"Bertrand","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2023061310492327800_btab477-B4","doi-asserted-by":"crossref","first-page":"2066","DOI":"10.1109\/TCBB.2018.2844816","article-title":"Robust inductive matrix completion strategy to explore associations between lincrnas and human disease phenotypes","volume":"16","author":"Biswas","year":"2019","journal-title":"IEEE-ACM Trans. Comput. Biol. Bioinform"},{"key":"2023061310492327800_btab477-B5","doi-asserted-by":"crossref","first-page":"4256","DOI":"10.1093\/bioinformatics\/bty503","article-title":"Predicting mirna\u2013disease association based on inductive matrix completion","volume":"34","author":"Chen","year":"2018","journal-title":"Bioinformatics"},{"key":"2023061310492327800_btab477-B6","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1093\/bib\/bbv068","article-title":"Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes","volume":"17","author":"Cheng","year":"2016","journal-title":"Brief. Bioinform"},{"key":"2023061310492327800_btab477-B7","doi-asserted-by":"crossref","first-page":"D975","DOI":"10.1093\/nar\/gkv1314","article-title":"Driverdbv2: a database for human cancer driver gene research","volume":"44","author":"Chung","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2023061310492327800_btab477-B8","article-title":"Prodigy: personalized prioritization of driver genes","author":"Dinstag","year":"2019","journal-title":"Bioinformatics, 36, 1831\u20131839"},{"key":"2023061310492327800_btab477-B9","doi-asserted-by":"crossref","first-page":"108856","DOI":"10.1016\/j.biopha.2019.108856","article-title":"Identification of survival-related predictors in hepatocellular carcinoma through integrated genomic, transcriptomic, and proteomic analyses","volume":"114","author":"Dong","year":"2019","journal-title":"Biomed. Pharmacother"},{"key":"2023061310492327800_btab477-B10","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1038\/nrc3299","article-title":"From cancer genomes to oncogenic drivers, tumor dependencies and therapeutic targets","volume":"12","author":"Eifert","year":"2012","journal-title":"Nat. Rev. Cancer"},{"key":"2023061310492327800_btab477-B11","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1038\/nrc1299","article-title":"A census of human cancer genes","volume":"4","author":"Futreal","year":"2004","journal-title":"Nat. Rev. Cancer"},{"first-page":"321","year":"2016","author":"Gligorijevic","key":"2023061310492327800_btab477-B12"},{"key":"2023061310492327800_btab477-B13","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1038\/s41587-020-0546-8","article-title":"Visualizing and interpreting cancer genomics data via the xena platform","volume":"38","author":"Goldman","year":"2020","journal-title":"Nat. Biotechnol"},{"key":"2023061310492327800_btab477-B14","doi-asserted-by":"crossref","first-page":"1893","DOI":"10.1093\/bioinformatics\/bty006","article-title":"Discovering personalized driver mutation profiles of single samples in cancer by network control strategy","volume":"34","author":"Guo","year":"2018","journal-title":"Bioinformatics"},{"key":"2023061310492327800_btab477-B15","doi-asserted-by":"crossref","first-page":"e1007520","DOI":"10.1371\/journal.pcbi.1007520","article-title":"A novel network control model for identifying personalized driver genes in cancer","volume":"15","author":"Guo","year":"2019","journal-title":"PLoS Comput. Biol"},{"key":"2023061310492327800_btab477-B16","doi-asserted-by":"crossref","first-page":"e45","DOI":"10.1093\/nar\/gkz096","article-title":"Driverml: a machine learning algorithm for identifying driver genes in cancer sequencing studies","volume":"47","author":"Han","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023061310492327800_btab477-B17","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.1038\/nmeth.2651","article-title":"Network-based stratification of tumor mutations","volume":"10","author":"Hofree","year":"2013","journal-title":"Nat. Methods"},{"key":"2023061310492327800_btab477-B18","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/s13073-014-0056-8","article-title":"Dawnrank: discovering personalized driver genes in cancer","volume":"6","author":"Hou","year":"2014","journal-title":"Genome Med"},{"key":"2023061310492327800_btab477-B19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/nar\/gkn923","article-title":"Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists","volume":"37","author":"Huang","year":"2009","journal-title":"Nucleic Acids Res"},{"key":"2023061310492327800_btab477-B20","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1101\/gr.071852.107","article-title":"Protein networks in disease","volume":"18","author":"Ideker","year":"2008","journal-title":"Genome Res"},{"year":"2013","author":"Jain","key":"2023061310492327800_btab477-B21"},{"key":"2023061310492327800_btab477-B22","doi-asserted-by":"crossref","first-page":"S17","DOI":"10.1530\/eje.0.151s017","article-title":"Evidence for a link between igf-i and cancer","volume":"151 (Suppl. 1","author":"Jenkins","year":"2004","journal-title":"Eur. J. Endocrinol"},{"key":"2023061310492327800_btab477-B23","first-page":"203","volume-title":"Integrating Biological Networks for Drug Target Prediction and Prioritization","author":"Ji","year":"2019"},{"key":"2023061310492327800_btab477-B24","doi-asserted-by":"crossref","first-page":"D590","DOI":"10.1093\/nar\/gky962","article-title":"New approach for understanding genome variations in KEGG","volume":"47","author":"Kanehisa","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023061310492327800_btab477-B25","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1038\/nature12213","article-title":"Mutational heterogeneity in cancer and the search for new cancer-associated genes","volume":"499","author":"Lawrence","year":"2013","journal-title":"Nature"},{"key":"2023061310492327800_btab477-B26","doi-asserted-by":"crossref","first-page":"2065","DOI":"10.1016\/j.eswa.2013.09.005","article-title":"Facing the cold start problem in recommender systems","volume":"41","author":"Lika","year":"2014","journal-title":"Expert Syst. Appl"},{"key":"2023061310492327800_btab477-B27","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1038\/nature10011","article-title":"Controllability of complex networks","volume":"473","author":"Liu","year":"2011","journal-title":"Nature"},{"key":"2023061310492327800_btab477-B28","doi-asserted-by":"crossref","first-page":"2360","DOI":"10.1016\/j.bbadis.2018.01.027","article-title":"Whole-exome mutational and transcriptional landscapes of combined hepatocellular cholangiocarcinoma and intrahepatic cholangiocarcinoma reveal molecular diversity","volume":"1864","author":"Liu","year":"2018","journal-title":"Biochim. Biophys. Acta Mol. Basis Dis"},{"key":"2023061310492327800_btab477-B29","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1038\/s41467-019-08797-8","article-title":"Towards a data-integrated cell","volume":"10","author":"Malod-Dognin","year":"2019","journal-title":"Nat. Commun"},{"key":"2023061310492327800_btab477-B30","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1038\/nrg2841","article-title":"Advances in understanding cancer genomes through second-generation sequencing","volume":"11","author":"Meyerson","year":"2010","journal-title":"Nat. Rev. Genet"},{"key":"2023061310492327800_btab477-B31","doi-asserted-by":"crossref","first-page":"i60","DOI":"10.1093\/bioinformatics\/btu269","article-title":"Inductive matrix completion for predicting gene\u2013disease associations","volume":"30","author":"Natarajan","year":"2014","journal-title":"Bioinformatics"},{"key":"2023061310492327800_btab477-B32","doi-asserted-by":"crossref","first-page":"D529","DOI":"10.1093\/nar\/gky1079","article-title":"The biogrid interaction database: 2019 update","volume":"47","author":"Oughtred","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023061310492327800_btab477-B33","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1016\/j.cell.2011.03.001","article-title":"Principles and strategies for developing network models in cancer","volume":"144","author":"Pe\u2019er","year":"2011","journal-title":"Cell"},{"key":"2023061310492327800_btab477-B34","doi-asserted-by":"crossref","first-page":"e1007538","DOI":"10.1371\/journal.pcbi.1007538","article-title":"CBNA: a control theory based method for identifying coding and non-coding cancer drivers","volume":"15","author":"Pham","year":"2019","journal-title":"PLoS Comput. Biol"},{"key":"2023061310492327800_btab477-B35","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.ygeno.2008.05.003","article-title":"Finding common genes in multiple cancer types through meta-analysis of microarray experiments: a rank aggregation approach","volume":"92","author":"Pihur","year":"2008","journal-title":"Genomics"},{"key":"2023061310492327800_btab477-B36","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/gm524","article-title":"Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine","volume":"6","author":"Raphael","year":"2014","journal-title":"Genome Med"},{"key":"2023061310492327800_btab477-B37","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1038\/msb.2012.68","article-title":"Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers","volume":"9","author":"Reimand","year":"2013","journal-title":"Mol. Syst. Biol"},{"key":"2023061310492327800_btab477-B38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-018-1612-0","article-title":"The network of cancer genes (NCG): a comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens","volume":"20","author":"Repana","year":"2019","journal-title":"Genome Biol"},{"key":"2023061310492327800_btab477-B39","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1002\/emmm.201202388","article-title":"Journeys into the genome of cancer cells","volume":"5","author":"Stratton","year":"2013","journal-title":"EMBO Mol. Med"},{"key":"2023061310492327800_btab477-B40","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/nature07943","article-title":"The cancer genome","volume":"458","author":"Stratton","year":"2009","journal-title":"Nature"},{"key":"2023061310492327800_btab477-B41","doi-asserted-by":"crossref","first-page":"e73484","DOI":"10.1371\/journal.pone.0073484","article-title":"Identification and characterization of cancer mutations in Japanese lung adenocarcinoma without sequencing of normal tissue counterparts","volume":"8","author":"Suzuki","year":"2013","journal-title":"PLoS One"},{"key":"2023061310492327800_btab477-B42","doi-asserted-by":"crossref","first-page":"D607","DOI":"10.1093\/nar\/gky1131","article-title":"String v11: protein\u2013protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets","volume":"47","author":"Szklarczyk","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023061310492327800_btab477-B43","doi-asserted-by":"crossref","first-page":"2238","DOI":"10.1093\/bioinformatics\/btt395","article-title":"Oncodriveclust: exploiting the positional clustering of somatic mutations to identify cancer genes","volume":"29","author":"Tamborero","year":"2013","journal-title":"Bioinformatics"},{"key":"2023061310492327800_btab477-B44","doi-asserted-by":"crossref","first-page":"1546","DOI":"10.1126\/science.1235122","article-title":"Cancer genome landscapes","volume":"339","author":"Vogelstein","year":"2013","journal-title":"Science"},{"key":"2023061310492327800_btab477-B45","doi-asserted-by":"crossref","first-page":"49268","DOI":"10.18632\/oncotarget.10284","article-title":"Dynein axonemal heavy chain 8 promotes androgen receptor activity and associates with prostate cancer progression","volume":"7","author":"Wang","year":"2016","journal-title":"Oncotarget"},{"key":"2023061310492327800_btab477-B46","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1038\/ng.2764","article-title":"The cancer genome atlas pan-cancer analysis project","volume":"45","author":"Weinstein","year":"2013","journal-title":"Nat. Genet"},{"key":"2023061310492327800_btab477-B47","first-page":"407","volume-title":"Annual Review of Genomics and Human Genetics","author":"Wong","year":"2011"},{"key":"2023061310492327800_btab477-B48","doi-asserted-by":"crossref","first-page":"2135","DOI":"10.1039\/C7MB00303J","article-title":"Discovering potential driver genes through an integrated model of somatic mutation profiles and gene functional information","volume":"13","author":"Xi","year":"2017","journal-title":"Mol. Biosyst"},{"key":"2023061310492327800_btab477-B49","doi-asserted-by":"crossref","first-page":"1855","DOI":"10.1093\/bioinformatics\/btz793","article-title":"Inferring subgroup-specific driver genes from heterogeneous cancer samples via subspace learning with subgroup indication","volume":"36","author":"Xi","year":"2020","journal-title":"Bioinformatics"},{"key":"2023061310492327800_btab477-B50","doi-asserted-by":"crossref","first-page":"976","DOI":"10.1093\/bioinformatics\/btq064","article-title":"Gosemsim: an R package for measuring semantic similarity among go terms and gene products","volume":"26","author":"Yu","year":"2010","journal-title":"Bioinformatics"},{"key":"2023061310492327800_btab477-B51","doi-asserted-by":"crossref","first-page":"13653","DOI":"10.1073\/pnas.1103360108","article-title":"Functional snp in the microrna-367 binding site in the 3\u2019utr of the calcium channel ryanodine receptor gene 3 (ryr3) affects breast cancer risk and calcification","volume":"108","author":"Zhang","year":"2011","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023061310492327800_btab477-B52","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.1093\/bioinformatics\/btaa062","article-title":"Drimc: an improved drug repositioning approach using Bayesian inductive matrix completion","volume":"36","author":"Zhang","year":"2020","journal-title":"Bioinformatics"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btab477\/39554481\/btab477.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/23\/4477\/50579598\/btab477.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/23\/4477\/50579598\/btab477.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T10:51:07Z","timestamp":1686653467000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/37\/23\/4477\/6310170"}},"subtitle":[],"editor":[{"given":"Teresa","family":"Przytycka","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,6,27]]},"references-count":52,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2021,12,7]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btab477","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"type":"print","value":"1367-4803"},{"type":"electronic","value":"1367-4811"}],"subject":[],"published-other":{"date-parts":[[2021,12,1]]},"published":{"date-parts":[[2021,6,27]]}}}