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A community computational challenge to predict the activity of pairs of compounds

Bansal, Mukesh ; Yang, Jichen ; Karan, Charles ; Menden, Michael P ; Costello, James C ; Tang, Hao ; Xiao, Guanghua ; Li, Yajuan ; Allen, Jeffrey ; Zhong, Rui ; Chen, Beibei ; Kim, Minsoo ; Wang, Tao ; Heiser, Laura M ; Realubit, Ronald ; Mattioli, Michela ; Alvarez, Mariano J ; Shen, Yao ; Gallahan, Daniel ; Singer, Dinah ; Saez-Rodriguez, Julio ; Xie, Yang ; Stolovitzky, Gustavo ; Califano, Andrea

Nature biotechnology, 2014-12, Vol.32 (12), p.1213-1222 [Periódico revisado por pares]

United States: Nature Publishing Group

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Título:
A community computational challenge to predict the activity of pairs of compounds
Autor: Bansal, Mukesh ; Yang, Jichen ; Karan, Charles ; Menden, Michael P ; Costello, James C ; Tang, Hao ; Xiao, Guanghua ; Li, Yajuan ; Allen, Jeffrey ; Zhong, Rui ; Chen, Beibei ; Kim, Minsoo ; Wang, Tao ; Heiser, Laura M ; Realubit, Ronald ; Mattioli, Michela ; Alvarez, Mariano J ; Shen, Yao ; Gallahan, Daniel ; Singer, Dinah ; Saez-Rodriguez, Julio ; Xie, Yang ; Stolovitzky, Gustavo ; Califano, Andrea
Assuntos: Algorithms ; B-Lymphocytes - drug effects ; Computer Simulation ; Computers ; Drug Combinations ; Drug Synergism ; Drug therapy ; Drug therapy, Combination ; Humans ; Pharmaceutical research ; Predictions
É parte de: Nature biotechnology, 2014-12, Vol.32 (12), p.1213-1222
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
These authors contributed equally to this work.
Present addresses: Department of Pharmacology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA (J.C.C.) and Department of Biomedical Engineering, Georgia Institute of Technology and Emory University (P.Q.).
Descrição: Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction.
Editor: United States: Nature Publishing Group
Idioma: Inglês

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A community computational challenge to predict the activity of pairs of compounds

United States: Nature Publishing Group

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