Sarvenaz Choobdar , Mehmet E. Ahsen , Jake Crawford , Mattia Tomasoni , David Lamparter , Junyuan Lin , Benjamin Hescott , Xiaozhe Hu , Johnathan Mercer , Ted Natoli , Rajiv Narayan , The DREAM Module Identification Challenge Consortium , Aravind Subramanian , Gustavo Stolovitzky , Zoltán Kutalik , Kasper Lage , Donna K. Slonim , Julio Saez-Rodriguez , Lenore J. Cowen , Sven Bergmann , Daniel Marbach , bioRxiv (2018) .
Identification of modules in molecular networks is at the core of many current analysis methods in biomedical research. However, how well different approaches identify disease-relevant modules in different types of networks remains poorly understood. We launched the “Disease Module Identification DREAM Challenge”, an open competition to comprehensively assess module identification methods across diverse gene, protein and signaling networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies (GWAS). While a number of approaches were successful in terms of discovering complementary trait-associated modules, consensus predictions derived from the challenge submissions performed best. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets and correctly prioritize candidate disease genes. This community challenge establishes benchmarks, tools and guidelines for molecular network analysis to study human disease biology (https://synapse.org/modulechallenge).