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Nat Biotechnol. 2014 Dec;32(12):1213-22. doi: 10.1038/nbt.3052. Epub 2014 Nov 17.

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

Author information

1
1] Department of Systems Biology, Columbia University, New York, New York, USA. [2] Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, USA.
2
Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
3
Columbia Genome Center, High Throughput Screening Facility, Columbia University, New York, New York, USA.
4
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
5
Howard Hughes Medical Institute, Department of Biomedical Engineering and Center of Synthetic Biology, Boston University, Boston, Massachusetts, USA.
6
Department of Immunology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
7
1] Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA. [2] Simmons Comprehensive Cancer Center, University of Texas, Southwestern Medical Center, Texas, USA.
8
Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA.
9
Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), Milan, Italy.
10
Division of Cancer Biology, National Cancer Institute, Bethesda, Maryland, USA.
11
IBM Computational Biology Center, IBM, T.J. Watson Research Center, Yorktown Heights, New York, USA.
12
1] Department of Systems Biology, Columbia University, New York, New York, USA. [2] Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, USA. [3] Department of Biomedical Informatics, Columbia University, New York, New York, USA. [4] Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York, USA. [5] Institute for Cancer Genetics, Columbia University, New York, New York, USA. [6] Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York, USA.
13
Swiss Institute for Experimental Cancer Research (ISREC), Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland.
14
Departments of Genetics, Stanford University, Stanford, California, USA.
15
University of Minnesota, Minneapolis, Minnesota, USA.
16
1] Department of Computer Science, Princeton University, Princeton, New Jersey, USA. [2] Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA .
17
Unilever Centre, Cambridge University, Cambridge, UK.
18
Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts, USA.
19
Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, W. Lafayette, Indiana, USA.
20
1] Computer Science Department, University of Crete, Crete, Greece. [2] Institute of Computer Science, FORTH, Crete, Greece.
21
1] Department of Systems Biology, Columbia University, New York, New York, USA. [2] Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, USA. [3] Department of Biomedical Informatics, Columbia University, New York, New York, USA. [4] Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York, USA. [5] Institute for Cancer Genetics, Columbia University, New York, New York, USA. [6] Herbert Irving Comprehensive Cancer Cente
22
1] Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA. [2] Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, Michigan, USA. [3] Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, USA.
23
Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA.
24
Korea Advanced Institute of Science and Technology, Daejeon, Korea.
25
Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal.
26
Department of Medicine, Dan L. Duncan Center Division of Biostatistics, Baylor College of Medicine, Houston, Texas, USA.
27
Center for Integrated Bioinformatics, Drexel University, Philadelphia, Pennsylvania, USA.
28
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
29
Department of Information Engineering, University of Padova, Padova, Italy.
30
Department of Computer and Information Science, IUPUI, Indianapolis, Indiana, USA.
31
Jefferson Kimmel Cancer Center, Philadelphia, Pennsylvania, USA.
32
Center for Computational Biology and Bioinformatics, IU School of Medicine, Indianapolis, Indiana, USA.
33
Department of Oncology and Carbone Cancer Center, University of Wisconsin, Madison, Wisconsin, USA.
34
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA .
35
Department of Physics, University of Marburg, Marburg, Germany.
36
Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA.
37
Leiden Academic Center for Drug Research, University of Leiden, Leiden, the Netherlands.
38
Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland.
39
Izmir Institute of Technology, Izmir, Turkey.
40
1] Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Espoo, Finland. [2] Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.
41
1] Korea Advanced Institute of Science and Technology, Daejeon, Korea. [2] Korea Institute of Science and Technology Information, Daejeon, Korea.
42
1] Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. [2] CAS-MPG Partner Institute for Computational Biology, Key Laboratory of Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P. R. China.
43
1] Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. [2] Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, Texas, USA.
44
Departments of Statistics and of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin, USA.
45
European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
46
Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Espoo, Finland.
47
IBM Almaden Research Center, San Jose, California, USA.
48
Bindley Bioscience Center, Purdue University, W. Lafayette, Indiana, USA.
49
Embedded Systems Laboratory (ESL), Institute of Electrical Engineering, Swiss Federal Institute of Technology Lausanne (EPFL), Laussane, Switzerland.
50
1] Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA. [2] Department of Genetics, UNC Chapel Hill, Chapel Hill, North Carolina, USA.
51
Janssen Pharmaceutica, Beerse, Belgium.
52
1] Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. [2] Division of Biostatistics, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
53
National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.

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.

PMID:
25419740
PMCID:
PMC4399794
DOI:
10.1038/nbt.3052
[Indexed for MEDLINE]
Free PMC Article

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