Assessment of network module identification across complex diseases.

Journal: Nature methods

Volume: 16

Issue: 9

Year of Publication: 2019

Affiliated Institutions:  Department of Computational Biology, University of Lausanne, Lausanne, Switzerland. Icahn Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Department of Computer Science, Tufts University, Medford, MA, USA. Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland. Department of Mathematics, Tufts University, Medford, MA, USA. College of Computer and Information Science, Northeastern University, Boston, MA, USA. Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Broad Institute of MIT and Harvard, Cambridge, MA, USA. Swiss Institute of Bioinformatics, Lausanne, Switzerland. Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University, Bioquant, Heidelberg, Germany. Department of Computational Biology, University of Lausanne, Lausanne, Switzerland. sven.bergmann@unil.ch. Department of Computational Biology, University of Lausanne, Lausanne, Switzerland. daniel.marbach.dm@roche.com.

Abstract summary 

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.

Authors & Co-authors:  Choobdar Sarvenaz S Ahsen Mehmet E ME Crawford Jake J Tomasoni Mattia M Fang Tao T Lamparter David D Lin Junyuan J Hescott Benjamin B Hu Xiaozhe X Mercer Johnathan J Natoli Ted T Narayan Rajiv R Subramanian Aravind A Zhang Jitao D JD Stolovitzky Gustavo G Kutalik Zoltán Z Lage Kasper K Slonim Donna K DK Saez-Rodriguez Julio J Cowen Lenore J LJ Bergmann Sven S Marbach Daniel D

Study Outcome 

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Statistics
Citations :  Schadt EE. Molecular networks as sensors and drivers of common human diseases. Nature. 2009;461:218–223. doi: 10.1038/nature08454.
Authors :  23
Identifiers
Doi : 10.1038/s41592-019-0509-5
SSN : 1548-7105
Study Population
Male,Female
Mesh Terms
Algorithms
Other Terms
Study Design
Cross Sectional Study
Study Approach
Country of Study
Publication Country
United States