1. A community computational challenge to predict the activity of pairs of compounds
- Author
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Bansal, M, Yang, J, Karan, C, Menden, Mp, Costello, Jc, Tang, H, Xiao, G, Li, Y, Allen, J, Zhong, R, Chen, B, Kim, M, Wang, T, Heiser, Lm, Realubit, R, Mattioli, M, Alvarez, Mj, Shen, Y, Gallahan, D, Singer, D, Saez Rodriguez, J, Xie, Y, Stolovitzky, G, Califano, A, NCI DREAM Community: Jean Paul Abbuehl, NCI DREAM C. o. m. m. u. n. i. t. y., Jeffrey, Allen, Altman, Russ B., Shawn, Balcome, Mukesh, Bansal, Ana, Bell, Andreas, Bender, Bonnie, Berger, Jonathan, Bernard, Bieberich, Andrew A., Giorgos, Borboudakis, Andrea, Califano, Christina, Chan–, Beibei, Chen, Ting Huei Chen, Jaejoon, Choi, Luis Pedro Coelho, Costello, James C., Creighton, Chad J., Will, Dampier, Jo Davisson, V., Raamesh, Deshpande, Lixia, Diao, DI CAMILLO, Barbara, Murat, Dundar, Adam, Ertel, Cellworks, Group, Daniel, Gallahan, Goswami, Chirayu P., Assaf, Gottlieb, Gould, Michael N., Jonathan, Goya, Michael, Grau, Gray, Joe W., Heiser, Laura M., Hejase, Hussein A., Hoffmann, Michael F., Krisztian, Homicsko, Max, Homilius, Woochang, Hwang, Ijzerman, Adriaan P., Olli, Kallioniemi, Bilge, Karacali, Charles, Karan, Samuel, Kaski, Junho, Kim, Minsoo, Kim, Arjun, Krishnan, Junehawk, Lee, Young Suk Lee, Lenselink, Eelke B., Peter, Lenz, Lang, Li, Jun, Li, Yajuan, Li, Han, Liang, Michela, Mattioli, Menden, Michael P., John Patrick Mpindi, Myers, Chad L., Newton, Michael A., Overington, John P., Juuso, Parkkinen, Prill, Robert J., Jian, Peng, Richard, Pestell, Peng, Qiu, Bartek, Rajwa, Ronald, Realubit, Anguraj, Sadanandam, Julio Saez Rodriguez, Sambo, Francesco, Dinah, Singer, Gustavo, Stolovitzky, Arvind, Sridhar, Wei, Sun, Hao, Tang, Toffolo, GIANNA MARIA, Aydin, Tozeren, Troyanskaya, Olga G., Ioannis, Tsamardinos, van Vlijmen, Herman W. T., Tao, Wang, Wen, Wang, Wegner, Joerg K., Krister, Wennerberg, van Westen, Gerard J. P., Tian, Xia, Guanghua, Xiao, Yang, Xie, Jichen, Yang, Yang, Yang, Victoria, Yao, Yuan, Yuan, Haoyang, Zeng, Shihua, Zhang, Junfei, Zhao, Jian, Zhou, Rui, Zhong, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Zeng, Haoyang, TR11527, Karaçalı, Bilge, and Izmir Institute of Technology. Electronics and Communication Engineering
- Subjects
Computer science ,In silico ,Synergistic combinations ,Biomedical Engineering ,Bioengineering ,Computational biology ,Bioinformatics ,Applied Microbiology and Biotechnology ,Article ,Drug synergism ,Multiple time ,Humans ,Computer Simulation ,Computational challenges ,B-Lymphocytes ,Extramural ,Drug combinations ,Rank (computer programming) ,Drug Synergism ,Scoring metrics ,Drug Combinations ,Molecular Medicine ,Gene expression ,Algorithms ,Forecasting ,Biotechnology - 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., 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)
- Published
- 2014