Inferring causal molecular networks: empirical assessment through a community-based effort.

Journal: 
Nat Methods
Publication Year: 
2016
Authors: 
Steven M Hill
Laura M Heiser
Thomas Cokelaer
Michael Unger
Nicole K Nesser
Daniel E Carlin
Yang Zhang
Artem Sokolov
Evan O Paull
Chris K Wong
Kiley Graim
Adrian Bivol
Haizhou Wang
Fan Zhu
Bahman Afsari
Ludmila V Danilova
Alexander V Favorov
Wai Shing Lee
Dane Taylor
Chenyue W Hu
Byron L Long
David P Noren
Alexander J Bisberg
Gordon B Mills
Joe W Gray
Michael Kellen
Thea Norman
Stephen Friend
Amina A Qutub
Elana J Fertig
Yuanfang Guan
Mingzhou Song
Joshua M Stuart
Paul T Spellman
Heinz Koeppl
Gustavo Stolovitzky
Julio Saez-Rodriguez
Sach Mukherjee
PubMed link: 
26901648
Public Summary: 
It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be 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. Additional sub-challenges considered time-course prediction and visualization. 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.
Scientific Abstract: 
It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be 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. Additional sub-challenges considered time-course prediction and visualization. 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.