{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T07:09:29Z","timestamp":1771744169078,"version":"3.50.1"},"reference-count":63,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61873080"],"award-info":[{"award-number":["61873080"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61673151"],"award-info":[{"award-number":["61673151"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LY18A050004"],"award-info":[{"award-number":["LY18A050004"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LR18A050001"],"award-info":[{"award-number":["LR18A050001"]}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["HHSN261200800001E"],"award-info":[{"award-number":["HHSN261200800001E"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000016","name":"Department of Health and Human Services","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000016","id-type":"DOI","asserted-by":"publisher"}]},{"name":"US Government"},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Intramural Research Program"},{"DOI":"10.13039\/100000098","name":"NIH Clinical Center","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000098","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,4,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Tumor stratification has a wide range of biomedical and clinical applications, including diagnosis, prognosis and personalized treatment. However, cancer is always driven by the combination of mutated genes, which are highly heterogeneous across patients. Accurately subdividing the tumors into subtypes is challenging.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We developed a network-embedding based stratification (NES) methodology to identify clinically relevant patient subtypes from large-scale patients\u2019 somatic mutation profiles. The central hypothesis of NES is that two tumors would be classified into the same subtypes if their somatic mutated genes located in the similar network regions of the human interactome. We encoded the genes on the human protein\u2013protein interactome with a network embedding approach and constructed the patients\u2019 vectors by integrating the somatic mutation profiles of 7344 tumor exomes across 15 cancer types. We firstly adopted the lightGBM classification algorithm to train the patients\u2019 vectors. The AUC value is around 0.89 in the prediction of the patient\u2019s cancer type and around 0.78 in the prediction of the tumor stage within a specific cancer type. The high classification accuracy suggests that network embedding-based patients\u2019 features are reliable for dividing the patients. We conclude that we can cluster patients with a specific cancer type into several subtypes by using an unsupervised clustering algorithm to learn the patients\u2019 vectors. Among the 15 cancer types, the new patient clusters (subtypes) identified by the NES are significantly correlated with patient survival across 12 cancer types. In summary, this study offers a powerful network-based deep learning methodology for personalized cancer medicine.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Source code and data can be downloaded from https:\/\/github.com\/ChengF-Lab\/NES.<\/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\/btaa1099","type":"journal-article","created":{"date-parts":[[2020,12,28]],"date-time":"2020-12-28T20:14:06Z","timestamp":1609186446000},"page":"82-88","source":"Crossref","is-referenced-by-count":19,"title":["A network-based deep learning methodology for stratification of tumor mutations"],"prefix":"10.1093","volume":"37","author":[{"given":"Chuang","family":"Liu","sequence":"first","affiliation":[{"name":"Alibaba Research Center for Complexity Sciences, Hangzhou Normal University , Hangzhou 311121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Han","sequence":"additional","affiliation":[{"name":"Alibaba Research Center for Complexity Sciences, Hangzhou Normal University , Hangzhou 311121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zi-Ke","family":"Zhang","sequence":"additional","affiliation":[{"name":"Alibaba Research Center for Complexity Sciences, Hangzhou Normal University , Hangzhou 311121, China"},{"name":"College of Media and International Culture, Zhejiang University , Hangzhou 310028, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruth","family":"Nussinov","sequence":"additional","affiliation":[{"name":"Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick , Frederick, MD 21702, USA"},{"name":"Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University , Tel Aviv 69978, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1736-2847","authenticated-orcid":false,"given":"Feixiong","family":"Cheng","sequence":"additional","affiliation":[{"name":"Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic , Cleveland, OH 44195, USA"},{"name":"Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University , Cleveland, OH 44195, USA"},{"name":"Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine , Cleveland, OH 44106, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,1,8]]},"reference":[{"key":"2023051510491955400_btaa1099-B1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1038\/s41698-019-0078-1","article-title":"Artificial intelligence for precision oncology: beyond patient stratification","volume":"3","author":"Azuaje","year":"2019","journal-title":"NPJ Precision Oncol"},{"key":"2023051510491955400_btaa1099-B2","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.cell.2018.02.060","article-title":"Comprehensive characterization of cancer driver genes and mutations","volume":"173","author":"Bailey","year":"2018","journal-title":"Cell"},{"key":"2023051510491955400_btaa1099-B3","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1038\/nature12627","article-title":"Tumour heterogeneity in the clinic","volume":"501","author":"Bedard","year":"2013","journal-title":"Nature"},{"key":"2023051510491955400_btaa1099-B4","doi-asserted-by":"crossref","first-page":"D1228","DOI":"10.1093\/nar\/gks1147","article-title":"InnateDB: systems biology of innate immunity and beyond\u2013recent updates and continuing curation","volume":"41","author":"Breuer","year":"2013","journal-title":"Nucleic Acids Res"},{"key":"2023051510491955400_btaa1099-B5","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1016\/j.cell.2018.05.015","article-title":"Next-generation machine learning for biological networks","volume":"173","author":"Camacho","year":"2018","journal-title":"Cell"},{"key":"2023051510491955400_btaa1099-B6","doi-asserted-by":"crossref","first-page":"D470","DOI":"10.1093\/nar\/gku1204","article-title":"The BioGRID interaction database: 2015 update","volume":"43","author":"Chatr-Aryamontri","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2023051510491955400_btaa1099-B7","first-page":"785","author":"Chen","year":"2016"},{"key":"2023051510491955400_btaa1099-B8","first-page":"307","author":"Chen","year":"2018"},{"key":"2023051510491955400_btaa1099-B9","doi-asserted-by":"crossref","first-page":"2156","DOI":"10.1093\/molbev\/msu167","article-title":"Studying tumorigenesis through network evolution and somatic mutational perturbations in the cancer interactome","volume":"31","author":"Cheng","year":"2014","journal-title":"Mol. Biol. Evol"},{"key":"2023051510491955400_btaa1099-B10","doi-asserted-by":"crossref","first-page":"3697","DOI":"10.18632\/oncotarget.1984","article-title":"Quantitative network mapping of the human kinome interactome reveals new clues for rational kinase inhibitor discovery and individualized cancer therapy","volume":"5","author":"Cheng","year":"2014","journal-title":"Oncotarget"},{"key":"2023051510491955400_btaa1099-B11","doi-asserted-by":"crossref","first-page":"e1004497","DOI":"10.1371\/journal.pcbi.1004497","article-title":"A gene gravity model for the evolution of cancer genomes: a study of 3,000 cancer genomes across 9 cancer types","volume":"11","author":"Cheng","year":"2015","journal-title":"PLoS Comput. Biol"},{"key":"2023051510491955400_btaa1099-B12","doi-asserted-by":"crossref","first-page":"3476","DOI":"10.1038\/s41467-019-10744-6","article-title":"A genome-wide positioning systems network algorithm for in silico drug repurposing","volume":"10","author":"Cheng","year":"2019","journal-title":"Nat. Commun"},{"key":"2023051510491955400_btaa1099-B13","doi-asserted-by":"crossref","first-page":"D862","DOI":"10.1093\/nar\/gkr967","article-title":"PINA v2.0: mining interactome modules","volume":"40","author":"Cowley","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2023051510491955400_btaa1099-B14","doi-asserted-by":"crossref","first-page":"D261","DOI":"10.1093\/nar\/gkq1104","article-title":"Phospho.ELM: a database of phosphorylation sites\u2014update 2011","volume":"39","author":"Dinkel","year":"2011","journal-title":"Nucleic Acids Res"},{"key":"2023051510491955400_btaa1099-B15","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/s41576-019-0122-6","article-title":"Deep learning: new computational modelling techniques for genomics","volume":"20","author":"Eraslan","year":"2019","journal-title":"Nat. Rev. Genet"},{"key":"2023051510491955400_btaa1099-B16","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"2023051510491955400_btaa1099-B17","first-page":"226","author":"Ester","year":"1996"},{"key":"2023051510491955400_btaa1099-B18","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/1752-0509-7-7","article-title":"SignaLink 2\u2014a signaling pathway resource with multilayered regulatory networks","volume":"7","author":"Fazekas","year":"2013","journal-title":"BMC Syst. Biol"},{"key":"2023051510491955400_btaa1099-B19","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1056\/NEJMoa1113205","article-title":"Intra tumor heterogeneity and branched evolution revealed by multiregion sequencing","volume":"366","author":"Gerlinger","year":"2012","journal-title":"New Engl. J. Med"},{"key":"2023051510491955400_btaa1099-B20","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.knosys.2018.03.022","article-title":"Graph embedding techniques, applications and performance: a survey","volume":"151","author":"Goyal","year":"2018","journal-title":"Knowl. Based Syst"},{"key":"2023051510491955400_btaa1099-B21","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.cell.2011.02.013","article-title":"Hallmarks of cancer: the next generation","volume":"144","author":"Hanahan","year":"2011","journal-title":"Cell"},{"key":"2023051510491955400_btaa1099-B22","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1038\/s41572-019-0111-2","article-title":"Breast cancer","volume":"5","author":"Harbeck","year":"2019","journal-title":"Nat. Rev. Dis. Primers"},{"key":"2023051510491955400_btaa1099-B23","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":"2023051510491955400_btaa1099-B24","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1038\/nmeth.4514","article-title":"NetSig: network-based discovery from cancer genomes","volume":"15","author":"Horn","year":"2018","journal-title":"Nat. Methods"},{"key":"2023051510491955400_btaa1099-B25","doi-asserted-by":"crossref","first-page":"D512","DOI":"10.1093\/nar\/gku1267","article-title":"PhosphoSitePlus, 2014: mutations, PTMs and recalibrations","volume":"43","author":"Hornbeck","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2023051510491955400_btaa1099-B26","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1093\/bioinformatics\/btt627","article-title":"PhosphoNetworks: a database for human phosphorylation networks","volume":"30","author":"Hu","year":"2014","journal-title":"Bioinformatics"},{"key":"2023051510491955400_btaa1099-B27","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1038\/nrg.2016.87","article-title":"Network biology concepts in complex disease comorbidities","volume":"17","author":"Hu","year":"2016","journal-title":"Nat. Rev. Genet"},{"key":"2023051510491955400_btaa1099-B28","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.cell.2015.06.043","article-title":"The BioPlex network: a systematic exploration of the human interactome","volume":"162","author":"Huttlin","year":"2015","journal-title":"Cell"},{"key":"2023051510491955400_btaa1099-B29","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1038\/nature08987","article-title":"International network of cancer genome projects","volume":"464","year":"2010","journal-title":"Nature"},{"key":"2023051510491955400_btaa1099-B30","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1038\/s42256-020-00236-4","article-title":"Drug discovery with explainable artificial intelligence","volume":"2","author":"Jimenez-Luna","year":"2020","journal-title":"Nat. Mach. Intell"},{"key":"2023051510491955400_btaa1099-B31","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1093\/bioinformatics\/btx624","article-title":"DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier","volume":"34","author":"Kulmanov","year":"2018","journal-title":"Bioinformatics"},{"key":"2023051510491955400_btaa1099-B32","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1038\/ng.3168","article-title":"Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes","volume":"47","author":"Leiserson","year":"2015","journal-title":"Nat. Genet"},{"key":"2023051510491955400_btaa1099-B33","doi-asserted-by":"crossref","first-page":"D857","DOI":"10.1093\/nar\/gkr930","article-title":"MINT, the molecular interaction database: 2012 update","volume":"40","author":"Licata","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2023051510491955400_btaa1099-B34","doi-asserted-by":"crossref","first-page":"e1007701","DOI":"10.1371\/journal.pcbi.1007701","article-title":"Individualized genetic network analysis reveals new therapeutic vulnerabilities in 6,700 cancer genomes","volume":"16","author":"Liu","year":"2020","journal-title":"PLoS Comput. Biol"},{"key":"2023051510491955400_btaa1099-B35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2019.12.004","article-title":"Computational network biology: data, models, and applications","volume":"846","author":"Liu","year":"2020","journal-title":"Phys. Rep"},{"key":"2023051510491955400_btaa1099-B36","doi-asserted-by":"crossref","first-page":"2691","DOI":"10.1093\/bioinformatics\/btx167","article-title":"Entropy-based consensus clustering for patient stratification","volume":"33","author":"Liu","year":"2017","journal-title":"Bioinformatics"},{"key":"2023051510491955400_btaa1099-B37","doi-asserted-by":"crossref","first-page":"D295","DOI":"10.1093\/nar\/gks1229","article-title":"DbPTM 3.0: an informative resource for investigating substrate site specificity and functional association of protein post-translational modifications","volume":"41","author":"Lu","year":"2013","journal-title":"Nucleic Acids Res"},{"key":"2023051510491955400_btaa1099-B38","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1038\/s41586-020-2188-x","article-title":"A reference map of the human binary protein interactome","volume":"580","author":"Luck","year":"2020","journal-title":"Nature"},{"key":"2023051510491955400_btaa1099-B39","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1159\/000012061","article-title":"Artificial neural networks applied to survival prediction in breast cancer","volume":"57","author":"Lundin","year":"1999","journal-title":"Oncology"},{"key":"2023051510491955400_btaa1099-B40","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1038\/nmeth.4627","article-title":"Using deep learning to model the hierarchical structure and function of a cell","volume":"15","author":"Ma","year":"2018","journal-title":"Nat. Methods"},{"key":"2023051510491955400_btaa1099-B41","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1038\/nature12624","article-title":"Tumor heterogeneity and cancer cell plasticity","volume":"501","author":"Meacham","year":"2013","journal-title":"Nature"},{"key":"2023051510491955400_btaa1099-B42","doi-asserted-by":"crossref","first-page":"1577","DOI":"10.1093\/bioinformatics\/btt181","article-title":"INstruct: a database of high-quality 3D structurally resolved protein interactome networks","volume":"29","author":"Meyer","year":"2013","journal-title":"Bioinformatics"},{"key":"2023051510491955400_btaa1099-B43","author":"Mikolov","year":"2013"},{"key":"2023051510491955400_btaa1099-B44","doi-asserted-by":"crossref","first-page":"381","DOI":"10.3389\/fgene.2019.00381","article-title":"To embed or not: network embedding as a paradigm in computational biology","volume":"10","author":"Nelson","year":"2019","journal-title":"Front. Genet"},{"key":"2023051510491955400_btaa1099-B45","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s12551-018-0496-2","article-title":"Precision medicine review: rare driver mutations and their biophysical classification","volume":"11","author":"Nussinov","year":"2019","journal-title":"Biophys. Rev"},{"key":"2023051510491955400_btaa1099-B46","doi-asserted-by":"crossref","first-page":"D358","DOI":"10.1093\/nar\/gkt1115","article-title":"The MIntAct project\u2013IntAct as a common curation platform for 11 molecular interaction databases","volume":"42","author":"Orchard","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2023051510491955400_btaa1099-B47","doi-asserted-by":"crossref","first-page":"226","DOI":"10.3389\/fgene.2019.00226","article-title":"Predicting parkinson\u2019s disease genes based on Node2vec and autoencoder","volume":"10","author":"Peng","year":"2019","journal-title":"Front. Genet"},{"key":"2023051510491955400_btaa1099-B48","doi-asserted-by":"crossref","first-page":"D497","DOI":"10.1093\/nar\/gkh070","article-title":"Human protein reference database as a discovery resource for proteomics","volume":"32","author":"Peri","year":"2004","journal-title":"Nucleic Acids Res"},{"key":"2023051510491955400_btaa1099-B49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2500489","article-title":"Addressing big data time series: mining trillions of time series subsequences under dynamic time warping","volume":"7","author":"Rakthanmanon","year":"2013","journal-title":"ACM. Trans. Knowl. Discov"},{"key":"2023051510491955400_btaa1099-B50","first-page":"1812","article-title":"Gene expression profiling in breast cancer: classification, prognostication, and prediction","volume":"378","author":"Reis-Filho","year":"2011","journal-title":"Lacet"},{"key":"2023051510491955400_btaa1099-B51","first-page":"385","author":"Ribeiro","year":"2017"},{"key":"2023051510491955400_btaa1099-B52","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1016\/j.cell.2014.10.050","article-title":"A proteome-scale map of the human interactome network","volume":"159","author":"Rolland","year":"2014","journal-title":"Cell"},{"key":"2023051510491955400_btaa1099-B53","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1038\/ncponc0908","article-title":"Origins of breast cancer subtypes and therapeutic implications","volume":"4","author":"Sims","year":"2007","journal-title":"Nat. Clin. Pract. Oncol"},{"key":"2023051510491955400_btaa1099-B54","doi-asserted-by":"crossref","first-page":"14330","DOI":"10.1073\/pnas.1616440113","article-title":"Evaluating the evaluation of cancer driver genes","volume":"113","author":"Tokheim","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023051510491955400_btaa1099-B55","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med"},{"key":"2023051510491955400_btaa1099-B56","first-page":"2579","article-title":"Visualizing high-dimensional data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res"},{"key":"2023051510491955400_btaa1099-B57","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":"2023051510491955400_btaa1099-B58","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1038\/d41586-018-02881-7","article-title":"Machine learning classifies cancer","volume":"555","author":"Wong","year":"2018","journal-title":"Nature"},{"key":"2023051510491955400_btaa1099-B59","doi-asserted-by":"crossref","first-page":"i484","DOI":"10.1093\/bioinformatics\/bty247","article-title":"Classifying tumors by supervised network propagation","volume":"34","author":"Zhang","year":"2018","journal-title":"Bioinformatics"},{"key":"2023051510491955400_btaa1099-B60","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"},{"key":"2023051510491955400_btaa1099-B61","doi-asserted-by":"crossref","first-page":"1775","DOI":"10.1039\/C9SC04336E","article-title":"Target identification among known drugs by deep learning from heterogeneous networks","volume":"11","author":"Zeng","year":"2020","journal-title":"Chem. Sci"},{"key":"2023051510491955400_btaa1099-B62","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1038\/nmeth.2956","article-title":"TCGA-assembler: open-source software for retrieving and processing TCGA data","volume":"11","author":"Zhu","year":"2014","journal-title":"Nat. Methods"},{"key":"2023051510491955400_btaa1099-B63","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1093\/bioinformatics\/btx160","article-title":"Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations","volume":"33","author":"Zong","year":"2017","journal-title":"Bioinformatics"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btaa1099\/35924890\/btaa1099.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/1\/82\/50321859\/btaa1099.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/1\/82\/50321859\/btaa1099.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T10:52:21Z","timestamp":1684147941000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/37\/1\/82\/6069567"}},"subtitle":[],"editor":[{"given":"Pier","family":"Luigi Martelli","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,1,1]]},"references-count":63,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,4,9]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaa1099","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,1,1]]},"published":{"date-parts":[[2021,1,1]]}}}