Prediction of human population responses to toxic compounds by a collaborative competition.

Federica Eduati Lara M Mangravite Tao Wang Hao Tang J Christopher Bare 1 Rui Huang 2 Thea Norman Mike Kellen Michael P Menden Yang Yang 3 Xiaowei Zhan Rui Zhong 4 Guanghua Xiao 5 Menghang Xia 6 Nour Abdo Oksana Kosyk Stephen Friend Gustavo Stolovitzky Allen Dearry Raymond R Tice Anton Simeonov Ivan Rusyn Fred A Wright Yang Xie 2 Salvatore Alaimo Alicia Amadoz Muhammad Ammad-Ud-Din Chloé-Agathe Azencott 7, 8 Jaume Bacardit Pelham Barron Elsa Bernard 9 Andreas Beyer 10 Shao Bin Alena Van Bömmel Karsten Borgwardt April M Brys Brian Caffrey Jeffrey Chang Jungsoo Chang Eleni G Christodoulou Mathieu Clément-Ziza 11 Trevor Cohen Marianne Cowherd Sofie Demeyer 12 Joaquin Dopazo 13, 14, 15 Joel D Elhard Andre O Falcao 16 Alfredo Ferro 17 David A Friedenberg Rosalba Giugno Yunguo Gong 18, 19 Jenni W Gorospe Courtney A Granville Dominik Grimm Matthias Heinig 20 Rosa D Hernansaiz Sepp Hochreiter Hua Huang 21 Matthew Huska Yunlong Jiao Günter Klambauer Michael Kuhn Miron Bartosz Kursa Rintu Kutum Nicola Lazzarini Inhan Lee 22 Michael K K Leung Weng Khong Lim 23 Charlie Liu Felipe Llinares López 24 Alessandro Mammana Andreas Mayr Tom Michoel Misael Mongiovì Jonathan D Moore 25 Ravi Narasimhan Stephen Opiyo Gaurav Pandey 26 Andrea L Peabody Juliane Perner Alfredo Pulvirenti Konrad Rawlik Susanne Reinhardt 27 Carol G Riffle Douglas Ruderfer Aaron J Sander 28 Richard S Savage Erwan Scornet 29 Patricia Sebastian-Leon Roded Sharan Carl Johann Simon-Gabriel Veronique Stoven 7, 8 Jingchun Sun 30 Ana L Teixeira Albert Tenesa 31 Jean-Philippe Vert 8, 7 Martin Vingron Thomas Walter 7, 8 Sean Whalen Zofia Wiśniewska Yonghui Wu 32 Hua Xu 33 Shihua Zhang Junfei Zhao 34 W Jim Zheng 35 Dai Ziwei Julio Saez-Rodriguez 36, 37 Xiaowei Xia 38 Guanghua Xia 39
16 CA3 - Computational Intelligence Research Group [Caparica]
UNINOVA - Universidade Nova de Lisboa, CTS - Centre of Technology and Systems
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.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-01428019
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Soumis le : vendredi 6 janvier 2017 - 11:44:05
Dernière modification le : mercredi 15 mai 2019 - 03:55:58

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Federica Eduati, Lara M Mangravite, Tao Wang, Hao Tang, J Christopher Bare, et al.. Prediction of human population responses to toxic compounds by a collaborative competition.. Nature Biotechnology, Nature Publishing Group, 2015, 33 (9), pp.933-40. ⟨10.1038/nbt.3299⟩. ⟨hal-01428019⟩

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