A community computational challenge to predict the activity of pairs of compounds
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Berger_A community.pdf
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Author(s)
Zeng, Haoyang
Date Issued
November 2014
Journal
Nature Biotechnology
Publisher
Nature Publishing Group
Citation
Bansal, Mukesh, Jichen Yang, Charles Karan, Michael P Menden, James C Costello, Hao Tang, Guanghua Xiao, et al. “A Community Computational Challenge to Predict the Activity of Pairs of Compounds.” Nature Biotechnology 32, no. 12 (November 17, 2014): 1213–1222. © 2014 Nature America, Inc.
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Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1038/nbt.3052
