TY - JOUR
T1 - Assessment of network module identification across complex 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, Johnathan
AU - Natoli, Ted
AU - Narayan, Rajiv
AU - Aicheler, Fabian
AU - Amoroso, Nicola
AU - Arenas, Alex
AU - Azhagesan, Karthik
AU - Baker, Aaron
AU - Banf, Michael
AU - Batzoglou, Serafim
AU - Baudot, Anaïs
AU - Bellotti, Roberto
AU - Bergmann, Sven
AU - Boroevich, Keith A.
AU - Brun, Christine
AU - Cai, Stanley
AU - Caldera, Michael
AU - Calderone, Alberto
AU - Cesareni, Gianni
AU - Chen, Weiqi
AU - Chichester, Christine
AU - Choobdar, Sarvenaz
AU - Cowen, Lenore
AU - Crawford, Jake
AU - Cui, Hongzhu
AU - Dao, Phuong
AU - De Domenico, Manlio
AU - Dhroso, Andi
AU - Didier, Gilles
AU - Divine, Mathew
AU - del Sol, Antonio
AU - Fang, Tao
AU - Feng, Xuyang
AU - Flores-Canales, Jose C.
AU - Fortunato, Santo
AU - Gitter, Anthony
AU - Gorska, Anna
AU - Guan, Yuanfang
AU - Guénoche, Alain
AU - Kunji, Khalid
AU - Ullah, Ehsan
N1 - Publisher Copyright:
© 2019, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2019/9/1
Y1 - 2019/9/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85071733655&partnerID=8YFLogxK
U2 - 10.1038/s41592-019-0509-5
DO - 10.1038/s41592-019-0509-5
M3 - Article
C2 - 31471613
AN - SCOPUS:85071733655
SN - 1548-7091
VL - 16
SP - 843
EP - 852
JO - Nature Methods
JF - Nature Methods
IS - 9
ER -