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Physiological and disease processes are typically not driven by a single gene, but multiple genes that interact within molecular modules or pathways. Identification of such modules in gene or protein networks is at the core of many current analysis methods in biomedical research. However, how well different methods perform to uncover biologically relevant modules in different types of networks remains poorly understood. The Disease Module Identification DREAM Challenge is an open community effort to:

  1. Systematically assess module identification methods on a panel of diverse state-of-the-art genomic networks
  2. Discover novel network modules/pathways underlying complex diseases


We have compiled a unique collection of a dozen as yet unpublished genomic networks for human, which were contributed by eight different groups for this challenge. (See Sharing a Network to learn more about contributing networks for this or future challenges). This collection includes state-of-the-art protein-protein interaction networks, signaling networks, regulatory networks and co-expression networks, among others.

Participants are challenged to apply network module identification methods (also known as community detection or graph clustering methods) to predict functional modules based on network topology. (The networks will be provided in anonymized form to enable blinded assessment.) Teams may participate in one or both of the following sub-challenges:

  • Sub-challenge 1: Identify modules for each network individually
  • Sub-challenge 2: Identify integrated modules from multiple networks


Choobdar, Sarvenaz, Mehmet E. Ahsen, Jake Crawford, Mattia Tomasoni, Tao Fang, David Lamparter, Junyuan Lin, et al. “Assessment of Network Module Identification across Complex Diseases.” Nature Methods 16, no. 9 (2019): 843–52. https://doi.org/10.1038/s41592-019-0509-5. Download