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• W2108933868 abstract "A community of researchers report the lessons learned from applying 44 algorithms to predict drug sensitivity in cancer cell lines using genomic, epigenetic and proteomic datasets Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods." @default.
• W2108933868 created "2016-06-24" @default.
• W2108933868 creator A5019591004 @default.
• W2108933868 date "2014-06-01" @default.
• W2108933868 modified "2023-10-06" @default.
• W2108933868 title "A community effort to assess and improve drug sensitivity prediction algorithms" @default.
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• W2108933868 doi "https://doi.org/10.1038/nbt.2877" @default.
• W2108933868 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4547623" @default.
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• W2108933868 hasPublicationYear "2014" @default.
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