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Target Inhibition Networks: Predicting Selective Combinations of Druggable Targets to Block Cancer Survival Pathways.

, , , , , , and . PLoS Computational Biology, (2013)

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Target Inhibition Networks: Predicting Selective Combinations of Druggable Targets to Block Cancer Survival Pathways., , , , , , and . PLoS Computational Biology, (2013)Comprehensive data-driven analysis of the impact of chemoinformatic structure on the genome-wide biological response profiles of cancer cells to 1159 drugs., , , , , , , , and . BMC Bioinformatics, (2012)Impact of normalization methods on high-throughput screening data with high hit rates and drug testing with dose-response data., , , , , , , , and . Bioinformatics, 31 (23): 3815-3821 (2015)Identification of structural features in chemicals associated with cancer drug response: a systematic data-driven analysis., , , , , and . Bioinformatics, 30 (17): 497-504 (2014)Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach., , , and . Bioinformatics, 34 (8): 1353-1362 (2018)Drug Target Commons 2.0: a community platform for systematic analysis of drug-target interaction profiles., , , , , , , , and . Database, (2018)Making Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis., , , , , , and . Journal of Chemical Information and Modeling, 54 (3): 735-743 (2014)Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression., , , and . Bioinformatics, 33 (14): i359-i368 (2017)Minimal information for chemosensitivity assays (MICHA): a next-generation pipeline to enable the FAIRification of drug screening experiments., , , , , , , , , and 9 other author(s). Briefings Bioinform., (2022)TIMMA-R: an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples., , , and . Bioinformatics, 31 (11): 1866-1868 (2015)