{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:45:13Z","timestamp":1740185113382,"version":"3.37.3"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"23","license":[{"start":{"date-parts":[[2018,6,15]],"date-time":"2018-06-15T00:00:00Z","timestamp":1529020800000},"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\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1452656"],"award-info":[{"award-number":["1452656"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000957","name":"Alzheimer\u2019s Association","doi-asserted-by":"crossref","award":["BAND-15-367116"],"award-info":[{"award-number":["BAND-15-367116"]}],"id":[{"id":"10.13039\/100000957","id-type":"DOI","asserted-by":"crossref"}]},{"name":"YFG"},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P30ES17885","U24CA210967"],"award-info":[{"award-number":["P30ES17885","U24CA210967"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Finding driver genes that are responsible for the aberrant proliferation rate of cancer cells is informative for both cancer research and the development of targeted drugs. The established experimental and computational methods are labor-intensive. To make algorithms feasible in real clinical settings, methods that can predict driver genes using less experimental data are urgently needed.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We designed an effective feature selection method and used Support Vector Machines (SVM) to predict the essentiality of the potential driver genes in cancer cell lines with only 10 genes as features. The accuracy of our predictions was the highest in the Broad-DREAM Gene Essentiality Prediction Challenge. We also found a set of genes whose essentiality could be predicted much more accurately than others, which we called Accurately Predicted (AP) genes. Our method can serve as a new way of assessing the essentiality of genes in cancer cells.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The raw data that support the findings of this study are available at Synapse. https:\/\/www.synapse.org\/#! Synapse: syn2384331\/wiki\/62825. Source code is available at GitHub. https:\/\/github.com\/GuanLab\/DREAM-Gene-Essentiality-Challenge.<\/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\/bty467","type":"journal-article","created":{"date-parts":[[2018,6,12]],"date-time":"2018-06-12T19:10:33Z","timestamp":1528830633000},"page":"3975-3982","source":"Crossref","is-referenced-by-count":4,"title":["Prioritizing predictive biomarkers for gene essentiality in cancer cells with mRNA expression data and DNA copy number profile"],"prefix":"10.1093","volume":"34","author":[{"given":"Yuanfang","family":"Guan","sequence":"first","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2559-184X","authenticated-orcid":false,"given":"Tingyang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjiu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gilbert S","family":"Omenn","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA"},{"name":"Departments of Internal Medicine and Human Genetics and School of Public Health, University of Michigan, Ann Arbor, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2018,6,15]]},"reference":[{"key":"2023012712250613100_bty467-B1","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"Barretina","year":"2012","journal-title":"Nature"},{"key":"2023012712250613100_bty467-B2","doi-asserted-by":"crossref","first-page":"2238","DOI":"10.1002\/hep.27030","article-title":"Functional genomics identified a novel protein tyrosine phos-phatase receptor type F-mediated growth inhibition in hepatocarcinogenesis","volume":"59","author":"Bera","year":"2014","journal-title":"Hepatology"},{"key":"2023012712250613100_bty467-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":"2023012712250613100_bty467-B4","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1136\/thoraxjnl-2013-204681","article-title":"Invited review DNA copy number changes as diagnostic tools for lung cancer","volume":"69","author":"Bowcock","year":"2014","journal-title":"Thorax"},{"key":"2023012712250613100_bty467-B5","doi-asserted-by":"crossref","first-page":"2463","DOI":"10.3892\/ol.2015.3107","article-title":"Expression changes of cell-cell adhesion-related genes in colorectal tumors","volume":"9","author":"Bujko","year":"2015","journal-title":"Oncol. 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