Journal article
A community computational challenge to predict the activity of pairs of compounds
Nature biotechnology, v 32(12), pp 1213-1222
Dec 2014
PMID: 25419740
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- A community computational challenge to predict the activity of pairs of compounds
- Creators
- Mukesh Bansal - Columbia UniversityJichen Yang - The University of Texas Southwestern Medical CenterCharles Karan - Columbia UniversityMichael P Menden - European Bioinformatics InstituteJames C Costello - Howard Hughes Medical InstituteHao Tang - The University of Texas Southwestern Medical CenterGuanghua Xiao - The University of Texas Southwestern Medical CenterYajuan Li - The University of Texas Southwestern Medical CenterJeffrey Allen - The University of Texas Southwestern Medical CenterRui Zhong - The University of Texas Southwestern Medical CenterBeibei Chen - The University of Texas Southwestern Medical CenterMinsoo Kim - 1] Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA. Simmons Comprehensive Cancer Center, University of Texas, Southwestern Medical Center, Texas, USATao Wang - The University of Texas Southwestern Medical CenterLaura M Heiser - Oregon Health & Science UniversityRonald Realubit - Columbia UniversityMichela Mattioli - Center for Genomic Science of IIT@SEMM Fondazione Istituto Italiano di Tecnologia (IIT) Milan ItalyMariano J Alvarez - Columbia UniversityYao Shen - Columbia UniversityDaniel Gallahan - National Cancer InstituteDinah Singer - National Cancer InstituteJulio Saez-Rodriguez - European Bioinformatics InstituteYang Xie - Simmons Comprehensive Cancer Center, University of Texas, Southwestern Medical Center, USAGustavo Stolovitzky - IBMAndrea Califano - Columbia UniversityNCI-DREAM CommunityWei Sun - Mechanical Engineering and Mechanics
- Publication Details
- Nature biotechnology, v 32(12), pp 1213-1222
- Publisher
- Springer Nature
- Grant note
- 3U01HL111566-02 / NHLBI NIH HHS 1U01CA164184-02 / NCI NIH HHS U01 CA168426 / NCI NIH HHS Howard Hughes Medical Institute P50 CA098258 / NCI NIH HHS U01 CA164184 / NCI NIH HHS 104104 / Wellcome Trust U01 HL111566 / NHLBI NIH HHS U54 CA121852 / NCI NIH HHS U54 CA112970 / NCI NIH HHS P30 CA016672 / NCI NIH HHS R01 CA152301 / NCI NIH HHS 5R01CA152301 / NCI NIH HHS 5U54CA121852-08 / NCI NIH HHS P50 CA058207 / NCI NIH HHS R01 GM071966 / NIGMS NIH HHS R01 GM081871 / NIGMS NIH HHS T32 HG003284 / NHGRI NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Mechanical Engineering and Mechanics
- Web of Science ID
- WOS:000346156800023
- Scopus ID
- 2-s2.0-84924338899
- Other Identifier
- 991019167813704721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biotechnology & Applied Microbiology