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 9 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 23 Günter Klambauer Michael Kuhn Miron Bartosz Kursa Rintu Kutum Nicola Lazzarini Inhan Lee 24 Michael K K Leung Weng Khong Lim 25 Charlie Liu Felipe Llinares López 26 Alessandro Mammana Andreas Mayr Tom Michoel Misael Mongiovì Jonathan D Moore 27 Ravi Narasimhan Stephen Opiyo Gaurav Pandey 28 Andrea L Peabody Juliane Perner Alfredo Pulvirenti Konrad Rawlik Susanne Reinhardt 29 Carol G Riffle Douglas Ruderfer Aaron J Sander 30 Richard S Savage Erwan Scornet 31 Patricia Sebastian-Leon Roded Sharan Carl Johann Simon-Gabriel Veronique Stoven 7, 8 Jingchun Sun 32 Ana L Teixeira Albert Tenesa 33 Jean-Philippe Vert 8, 7 Martin Vingron Thomas Walter 7, 8 Sean Whalen Zofia Wiśniewska Yonghui Wu 34 Hua Xu 35 Shihua Zhang Junfei Zhao 36 W Jim Zheng 37 Dai Ziwei Julio Saez-Rodriguez 38, 39 Xiaowei Xia 40 Guanghua Xia 41
17 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.
Liste complète des métadonnées

https://hal-mines-paristech.archives-ouvertes.fr/hal-01428019
Contributeur : Thomas Walter <>
Soumis le : vendredi 6 janvier 2017 - 11:44:05
Dernière modification le : lundi 17 décembre 2018 - 01:27:31

Lien texte intégral

Identifiants

Citation

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〉

Partager

Métriques

Consultations de la notice

897