This challenge is designed to build predictive models of cytotoxicity as mediated by exposure to environmental toxicants and drugs. To approach this question, we will provide a dataset containing cytotoxicity estimates as measured in lymphoblastoid cell lines derived from 884 individuals following in vitro exposure to 156 chemical compounds. In subchallenge 1, participants will be asked to model interindividual variability in cytotoxicity based on genomic profiles in order to predict cytotoxicity in unknown individuals. In subchallenge 2, participants will be asked to predict population-level parameters of cytotoxicity across chemicals based on structural attributes of compounds in order to predict median cytotoxicity and mean variance in toxicity for unknown compounds.
Nature Biotechnology (http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3299.html).
Eduati F#, Mangravite LM#, Wang T#, Tang H#, Bare JC, Huang R, Norman T, Kellen M, Menden MP, Yang J, Zhan X, Zhong R, Xiao G, Xia M, Abdo N, Kosyk O, NIEHS-NCATS-UNC DREAM Toxicogenetics Collaboration, Friend S, Stolovitzky G, Dearry A, Tice RR, Simeonov A, Rusyn I, Wright FA, Xie Y, Saez-Rodriguez J. Prediction of human population responses to toxic compounds by a collaborative competition. Nat Biotechnol. 33, 933–940 (2015). doi:10.1038/nbt.3299. PMID: 26258538. (#co-first authors)
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