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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 7 Salvatore Alaimo Alicia Amadoz Muhammad Ammad-Ud-Din Chloé-Agathe Azencott 8, 9 Jaume Bacardit Pelham Barron Elsa Bernard 10 Andreas Beyer 11 Shao Bin Alena van Bömmel Karsten Borgwardt April M Brys Brian Caffrey Jeffrey Chang Jungsoo Chang Eleni G Christodoulou Mathieu Clément-Ziza 12 Trevor Cohen Marianne Cowherd Sofie Demeyer 13 Joaquin Dopazo 14, 15, 16 Joel D Elhard Andre O Falcao 17 Alfredo Ferro 18 David A Friedenberg Rosalba Giugno Yunguo Gong 19, 20 Jenni W Gorospe Courtney A Granville Dominik Grimm Matthias Heinig 21 Rosa D Hernansaiz Sepp Hochreiter Hua Huang 22 Matthew Huska Yunlong Jiao Günter Klambauer Michael Kuhn Miron Bartosz Kursa Rintu Kutum Nicola Lazzarini Inhan Lee 23 Michael K K Leung Weng Khong Lim 24 Charlie Liu Felipe Llinares López 25 Alessandro Mammana Andreas Mayr Tom Michoel Misael Mongiovì Jonathan D Moore 26 Ravi Narasimhan Stephen Opiyo Gaurav Pandey 27 Andrea L Peabody Juliane Perner Alfredo Pulvirenti Konrad Rawlik Susanne Reinhardt 28 Carol G Riffle Douglas Ruderfer Aaron J Sander 29 Richard S Savage Erwan Scornet 30 Patricia Sebastian-Leon Roded Sharan Carl Johann Simon-Gabriel Veronique Stoven 8, 9 Jingchun Sun 31 Ana L Teixeira Albert Tenesa 32 Jean-Philippe Vert 9, 8 Martin Vingron Thomas Walter 8, 9 Sean Whalen Zofia Wiśniewska Yonghui Wu 33 Hua Xu 34 Shihua Zhang Junfei Zhao 35 W Jim Zheng 36 Dai Ziwei Julio Saez-Rodriguez 37, 38 Xiaowei Xia 39 Guanghua Xia 40 
17 CA3 - Computational Intelligence Research Group
CTS - Centre of Technology and Systems, FCT NOVA - Faculdade de Ciências e Tecnologia = School of Science & Technology
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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Submitted on : Friday, January 6, 2017 - 11:44:05 AM
Last modification on : Thursday, September 1, 2022 - 3:56:58 AM

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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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