Highlight talk – Theme: Systems
Abstract
Inferring molecular networks is a central challenge in computational biology. However, it remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.
Authors
Steven M. Hill, MRC Biostatistics Unit, United Kingdom
Laura M. Heiser, Oregon Health and Science University, United States
Thomas Cokelaer, European Bioinformatics Institute (EMBL-EBI), United Kingdom
Michael Unger, ETH Zurich, Switzerland
Nicole K. Nesser, Oregon Health and Science University, United States
Daniel E. Carlin, University of California Santa Cruz, United States
Yang Zhang, New Mexico State University, United States
Artem Sokolov, University of California Santa Cruz, United States
Evan O. Paull, University of California Santa Cruz, United States
Chris K. Wong, University of California Santa Cruz, United States
Kiley Graim, University of California Santa Cruz, United States
Adrian Bivol, University of California Santa Cruz, United States
Haizhou Wang, New Mexico State University, United States
Fan Zhu, University of Michigan, United States
Bahman Afsari, Johns Hopkins University, United States
Ludmila V. Danilova, Johns Hopkins University, United States
Alexander V. Favorov, Johns Hopkins University, United States
Wai Shing Lee, Johns Hopkins University, United States
Dane Taylor, University of North Carolina, United States
Chenyue W. Hu, Rice University, United States
Byron L. Long, Rice University, United States
David P. Noren, Rice University, United States
Alexander J. Bisberg, Rice University, United States
Gordon B. Mills, MD Anderson Cancer Center, United States
Joe W. Gray, Oregon Health and Science University, United States
Michael Kellen, Sage Bionetworks, United States
Thea Norman, Sage Bionetworks, United States
Stephen Friend, Sage Bionetworks, United States
Amina A. Qutub, Rice University, United States
Elana J. Fertig, Johns Hopkins University, United States
Yuanfang Guan, University of Michigan, United States
Mingzhou Song, New Mexico State University, United States
Joshua M. Stuart, University of California Santa Cruz, United States
Paul T. Spellman, Oregon Health and Science University, United States
Heinz Koeppl, ETH Zurich, Switzerland
Gustavo Stolovitzky, IBM Translational Systems Biology and Nanobiotechnology, United States
Julio Saez-Rodriguez, European Bioinformatics Institute (EMBL-EBI), United Kingdom
Sach Mukherjee, MRC Biostatistics Unit, United Kingdom