Prediction of human population responses to toxic compounds by a collaborative competition View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-09

AUTHORS

Federica Eduati, Lara M Mangravite, Tao Wang, Hao Tang, J Christopher Bare, Ruili Huang, Thea Norman, Mike Kellen, Michael P Menden, Jichen Yang, Xiaowei Zhan, Rui Zhong, Guanghua Xiao, Menghang Xia, Nour Abdo, Oksana Kosyk, The NIEHS-NCATS-UNC DREAM Toxicogenetics Collaboration, Stephen Friend, Allen Dearry, Anton Simeonov, Raymond R Tice, Ivan Rusyn, Fred A Wright, Gustavo Stolovitzky, Yang Xie, Julio Saez-Rodriguez

ABSTRACT

The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal. More... »

PAGES

933-940

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/nbt.3299

    DOI

    http://dx.doi.org/10.1038/nbt.3299

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1012342197

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/26258538


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