{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T20:52:18Z","timestamp":1759092738725,"version":"3.37.3"},"reference-count":15,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2018,10,8]],"date-time":"2018-10-08T00:00:00Z","timestamp":1538956800000},"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\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["5R01CA152301","1R01GM115473","1R01CA172211"],"award-info":[{"award-number":["5R01CA152301","1R01GM115473","1R01CA172211"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004917","name":"Cancer Prevention Research Institute of Texas","doi-asserted-by":"publisher","award":["RP120732-C2"],"award-info":[{"award-number":["RP120732-C2"]}],"id":[{"id":"10.13039\/100004917","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["S15-82"],"award-info":[{"award-number":["S15-82"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,5,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Synergistic drug combinations are a promising approach to achieve a desirable therapeutic effect in complex diseases through the multi-target mechanism. However, in vivo screening of all possible multi-drug combinations remains cost-prohibitive. An effective and robust computational model to predict drug synergy in silico will greatly facilitate this process.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We developed DIGREM (Drug-Induced Genomic Response models for identification of Effective Multi-drug combinations), an online tool kit that can effectively predict drug synergy. DIGREM integrates DIGRE, IUPUI_CCBB, gene set-based and correlation-based models for users to predict synergistic drug combinations with dose\u2013response information and drug-treated gene expression profiles.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>http:\/\/lce.biohpc.swmed.edu\/drugcombination<\/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\/bty860","type":"journal-article","created":{"date-parts":[[2018,10,5]],"date-time":"2018-10-05T04:27:13Z","timestamp":1538713633000},"page":"1792-1794","source":"Crossref","is-referenced-by-count":9,"title":["DIGREM: an integrated web-based platform for detecting effective multi-drug combinations"],"prefix":"10.1093","volume":"35","author":[{"given":"Minzhe","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sangin","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Information and Statistics, Chungnam National University, Daejeon, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanghua","family":"Xiao","sequence":"additional","affiliation":[{"name":"Department of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA"},{"name":"Harold C. 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