TY - JOUR
T1 - Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases
AU - The DREAM Module Identification Challenge Consortium
AU - Choobdar, Sarvenaz
AU - Ahsen, Mehmet E.
AU - Crawford, Jake
AU - Tomasoni, Mattia
AU - Fang, Tao
AU - Lamparter, David
AU - Lin, Junyuan
AU - Hescott, Benjamin
AU - Hu, Xiaozhe
AU - Mercer, Jonathon
AU - Natoli, Ted
AU - Narayan, Rajiv
AU - Subramanian, Aravind
AU - Zhang, Jitao D.
AU - Stolovitzky, Gustavo
AU - Kutalik, Zoltán
AU - Lage, Kasper
AU - Slonim, Donna K.
AU - Saez-Rodriguez, Julio
AU - Cowen, Leonore J.
AU - Bergmann, Sven
AU - Marbach, Daniel
PY - 2019/5/13
Y1 - 2019/5/13
N2 - 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 gene and protein networks 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 (GWAS). Our critical assessment of 75 contributed module identification methods reveals novel 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 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).
AB - 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 gene and protein networks 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 (GWAS). Our critical assessment of 75 contributed module identification methods reveals novel 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 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).
KW - Network biology
KW - Module identification
KW - Community detection algorithms
KW - Pathway analysis
KW - Genome-wide association studies
KW - Crowdsourced challenge
KW - Open science
M3 - Article
SN - 1548-7091
JO - Nature Methods
JF - Nature Methods
ER -