Inferring Causal Molecular Networks: Empirical Assessment through a Community-Based Effort

Hill SM, et al. Inferring causal molecular networks: Empirical assessment through a community-based effort. Nature Methods (2016) Apr; 13(4):310-318. doi: 10.1038/nmeth.3773. PMID: 26901648 PMCID: PMC4854847

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that 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 constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and 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 the causal validity of inferred molecular networks.

 

Collaborators: Afsari B, Al-Ouran R, Anton B, Arodz T, Sichani OA, Bagheri N, Berlow N, Bisberg AJ, Bivol A, Bohler A, Bonet J, Bonneau R, Budak G, Bunescu R, Caglar M, Cai B, Cai C, Carlin DE, Carlon A, Chen L, Ciaccio MF, Cokelaer T, Cooper G, Coort S, Creighton CJ, Daneshmand SM, de la Fuente A, Di Camillo B, Danilova LV, Dutta-Moscato J, Emmett K, Evelo C, Fassia MK, Favorov AV, Fertig EJ, Finkle JD, Finotello F, Friend S, Gao X, Gao J, Garcia-Garcia J, Ghosh S, Giaretta A, Graim K, Gray JW, Großeholz R, Guan Y, Guinney J, Hafemeister C, Hahn O, Haider S, Hase T, Heiser LM, Hill SM, Hodgson J, Hoff B, Hsu CH, Hu CW, Hu Y, Huang X, Jalili M, Jiang X, Kacprowski T, Kaderali L, Kang M, Kannan V, Kellen M, Kikuchi K, Kim DC, Kitano H, Knapp B, Komatsoulis G, Koeppl H, Krämer A, Kursa MB, Kutmon M, Lee WS, Li Y, Liang X, Liu Z, Liu Y, Long BL, Lu S, Lu X, Manfrini M, Matos MR, Meerzaman D, Mills GB, Min W, Mukherjee S, Müller CL, Neapolitan RE, Nesser NK, Noren DP, Norman T, Oliva B, Opiyo SO, Pal R, Palinkas A, Paull EO, Planas-Iglesias J, Poglayen D, Qutub AA, Saez-Rodriguez J, Sambo F, Sanavia T, Sharifi-Zarchi A, Slawek J, Sokolov A, Song M, Spellman PT, Streck A, Stolovitzky G, Strunz S, Stuart JM, Taylor D, Tegnér J, Thobe K, Toffolo GM, Trifoglio E, Unger M, Wan Q, Wang H, Welch L, Wong CK, Wu JJ, Xue AY, Yamanaka R, Yan C, Zairis S, Zengerling M, Zenil H, Zhang S, Zhang Y, Zhu F, Zi Z.

 

Publication Year: 
2016
Faculty Author: 
Publication Credits: 
Hill SM, et al.
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