Assessment of network module identification across complex diseases

The DREAM Module Identification Challenge Consortium, Sarvenaz Choobdar, Mehmet E. Ahsen, Jake Crawford, Mattia Tomasoni, Tao Fang, David Lamparter, Junyuan Lin, Benjamin Hescott, Xiaozhe Hu, Johnathan Mercer, Ted Natoli, Rajiv Narayan, Fabian Aicheler, Nicola Amoroso, Alex Arenas, Karthik Azhagesan, Aaron Baker, Michael Banf, Serafim BatzoglouAnaïs Baudot, Roberto Bellotti, Sven Bergmann, Keith A. Boroevich, Christine Brun, Stanley Cai, Michael Caldera, Alberto Calderone, Gianni Cesareni, Weiqi Chen, Christine Chichester, Sarvenaz Choobdar, Lenore Cowen, Jake Crawford, Hongzhu Cui, Phuong Dao, Manlio De Domenico, Andi Dhroso, Gilles Didier, Mathew Divine, Antonio del Sol, Tao Fang, Xuyang Feng, Jose C. Flores-Canales, Santo Fortunato, Anthony Gitter, Anna Gorska, Yuanfang Guan, Alain Guénoche, Sergio Gómez, Lu ZHANG

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)843-852
Number of pages10
JournalNature Methods
Volume16
Issue number9
DOIs
Publication statusPublished - 1 Sep 2019

Scopus Subject Areas

  • Biotechnology
  • Biochemistry
  • Molecular Biology
  • Cell Biology

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