Minzhe Zhang1, Sangin Lee2, Bo Yao1, Guanghua Xiao1,3, Lin Xu1,4, Yang Xie1,3. 1. Department of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA. 2. Department of Information and Statistics, Chungnam National University, Daejeon, Korea. 3. Harold C. Simmons Comprehensive Cancer Center, Dallas, TX, USA. 4. Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Abstract
MOTIVATION: 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. RESULTS: 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-response information and drug-treated gene expression profiles. AVAILABILITY AND IMPLEMENTATION: http://lce.biohpc.swmed.edu/drugcombination. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: 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. RESULTS: 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-response information and drug-treated gene expression profiles. AVAILABILITY AND IMPLEMENTATION: http://lce.biohpc.swmed.edu/drugcombination. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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