期刊
NATURE BIOTECHNOLOGY
卷 32, 期 12, 页码 1213-+出版社
NATURE PORTFOLIO
DOI: 10.1038/nbt.3052
关键词
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资金
- Multiscale Analysis of Genomic and Cellular Networks (MAGNet) [5U54CA121852-08]
- Library of Integrated Network-based Cellular Signatures Program (LINCS) [1U01CA164184-02, 3U01HL111566-02]
- National Institutes of Health (NIH) [5R01CA152301]
- Cancer Prevention and Research Institute of Texas (CPRIT) [RP101251]
- NIH, NCI [U54 CA112970]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1252648] Funding Source: National Science Foundation
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.
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