Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases
Authors: Choobdar Sarvenaz , Ahsen Mehmet E. , Crawford Jake , Tomasoni Mattia , Fang Tao , Lamparter David , Lin Junyuan , Hescott Benjamin , Hu Xiaozhe , Mercer Johnathan , Natoli Ted , Narayan Rajiv ,   The DREAM Module Identification Challenge Consortium, Subramanian Aravind , Zhang Jitao D. , Stolovitzky Gustavo , Kutalik Zoltán , Lage Kasper , Slonim Donna K. , Saez-Rodriguez Julio , Cowen Lenore J. , Bergmann Sven , Marbach Daniel , Aicheler Fabian , Amoroso Nicola , Arenas Alex , Azhagesan Karthik , Baker Aaron , Banf Michael , Batzoglou Serafim , Baudot Anaïs , Bellotti Roberto , Bergmann Sven , Boroevich Keith A. , Brun Christine , Cai Stanley , Caldera Michael , Calderone Alberto , Cesareni Gianni , Chen Weiqi , Chichester Christine , Choobdar Sarvenaz , Cowen Lenore , Crawford Jake , Cui Hongzhu , Dao Phuong , Domenico Manlio De , Dhroso Andi , Didier Gilles , Divine Mathew , Sol Antonio del , Fang Tao , Feng Xuyang , Flores-Canales Jose C. , Fortunato Santo , Gitter Anthony , Gorska Anna , Guan Yuanfang , Guénoche Alain , Gómez Sergio , Hamza Hatem , Hartmann András , He Shan , Heijs Anton , Heinrich Julian , Hescott Benjamin , Hu Xiaozhe , Hu Ying , Huang Xiaoqing , Hughitt V. Keith , Jeon Minji , Jeub Lucas , Johnson Nathan , Joo Keehyoung , Joung InSuk , Jung Sascha , Kalko Susana G. , Kamola Piotr J. , Kang Jaewoo , Kaveelerdpotjana Benjapun , Kim Minjun , Kim Yoo-Ah , Kohlbacher Oliver , Korkin Dmitry , Krzysztof Kiryluk , Kunji Khalid , Kutalik Zoltàn , Lage Kasper , Lamparter David , Lang-Brown Sean , Le Thuc Duy , Lee Jooyoung , Lee Sunwon , Lee Juyong , Li Dong , Li Jiuyong , Lin Junyuan , Liu Lin , Loizou Antonis , Luo Zhenhua , Lysenko Artem , Ma Tianle , Mall Raghvendra , Marbach Daniel , Mattia Tomasoni , Medvedovic Mario , Menche Jörg , Mercer Johnathan , Micarelli Elisa , Monaco Alfonso , Müller Felix , Narayan Rajiv , Narykov Oleksandr , Natoli Ted , Norman Thea , Park Sungjoon , Perfetto Livia , Perrin Dimitri , Pirrò Stefano , Przytycka Teresa M. , Qian Xiaoning , Raman Karthik , Ramazzotti Daniele , Ramsahai Emilie , Ravindran Balaraman , Rennert Philip , Saez-Rodriguez Julio , Schärfe Charlotta , Sharan Roded , Shi Ning , Shin Wonho , Shu Hai , Sinha Himanshu , Slonim Donna K. , Spinelli Lionel , Srinivasan Suhas , Subramanian Aravind , Suver Christine , Szklarczyk Damian , Tangaro Sabina , Thiagarajan Suresh , Tichit Laurent , Tiede Thorsten , Tripathi Beethika , Tsherniak Aviad , Tsunoda Tatsuhiko , Türei Dénes , Ullah Ehsan , Vahedi Golnaz , Valdeolivas Alberto , Vivek Jayaswal , Mering Christian von , Waagmeester Andra , Wang Bo , Wang Yijie , Weir Barbara A. , White Shana , Winkler Sebastian , Xu Ke , Xu Taosheng , Yan Chunhua , Yang Liuqing , Yu Kaixian , Yu Xiangtian , Zaffaroni Gaia , Zaslavskiy Mikhail , Zeng Tao , Zhang Jitao D. , Zhang Lu , Zhang Weijia , Zhang Lixia , Zhang Xinyu , Zhang Junpeng , Zhou Xin , Zhou Jiarui , Zhu Hongtu , Zhu Junjie , Zuccon Guido 
Published: February 2018

AbstractIdentification 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).