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Nat Methods. 2016 Apr;13(4):310-8. doi: 10.1038/nmeth.3773. Epub 2016 Feb 22.

Inferring causal molecular networks: empirical assessment through a community-based effort.

Author information

1
MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK.
2
Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA.
3
Center for Spatial Systems Biomedicine, Oregon Health and Science University, Portland, Oregon, USA.
4
Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon, USA.
5
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, UK.
6
Automatic Control Laboratory, ETH Zurich, Zurich, Switzerland.
7
Institute of Biochemistry, ETH Zurich, Zurich, Switzerland.
8
Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, Oregon, USA.
9
Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, California, USA.
10
Department of Computer Science, New Mexico State University, Las Cruces, New Mexico, USA.
11
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
12
Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA.
13
Laboratory of Systems Biology and Computational Genetics, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia.
14
Laboratory of Bioinformatics, Research Institute of Genetics and Selection of Industrial Microorganisms, Moscow, Russia.
15
Statistical and Applied Mathematical Sciences Institute, Research Triangle Park, North Carolina, USA.
16
Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina, USA.
17
Department of Bioengineering, Rice University, Houston, Texas, USA.
18
Department of Systems Biology, MD Anderson Cancer Center, Houston, Texas, USA.
19
Sage Bionetworks, Seattle, Washington, USA.
20
Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA.
21
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA.
22
IBM Translational Systems Biology and Nanobiotechnology, Yorktown Heights, New York, USA.
23
RWTH-Aachen University Hospital, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Aachen, Germany.
24
School of Clinical Medicine, University of Cambridge, Cambridge, UK.
25
German Centre for Neurodegenerative Diseases (DZNE), Bonn, Germany.

Abstract

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

PMID:
26901648
PMCID:
PMC4854847
DOI:
10.1038/nmeth.3773
[Indexed for MEDLINE]
Free PMC Article

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