Inferring causal molecular networks

Empirical assessment through a community-based effort

Steven M. Hill, Laura 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 & 18 others Byron L. Long, David P. Noren, Alexander J. Bisberg, Gordon Mills, Joe Gray, Michael Kellen, Thea Norman, Stephen Friend, Amina A. Qutub, Elana J. Fertig, Yuanfang Guan, Mingzhou Song, Joshua M. Stuart, Paul Spellman, Heinz Koeppl, Gustavo Stolovitzky, Julio Saez-Rodriguez, Sach Mukherjee

Research output: Contribution to journalArticle

80 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)310-318
Number of pages9
JournalNature Methods
Volume13
Issue number4
DOIs
StatePublished - Mar 30 2016

Fingerprint

Phosphoproteins
Learning
Nonlinear Dynamics
Computer Simulation
Visualization
Cells
Cell Line
Neoplasms

ASJC Scopus subject areas

  • Biotechnology
  • Molecular Biology
  • Biochemistry
  • Cell Biology

Cite this

Hill, S. M., Heiser, L., Cokelaer, T., Unger, M., Nesser, N. K., Carlin, D. E., ... Mukherjee, S. (2016). Inferring causal molecular networks: Empirical assessment through a community-based effort. Nature Methods, 13(4), 310-318. https://doi.org/10.1038/nmeth.3773

Inferring causal molecular networks : Empirical assessment through a community-based effort. / Hill, Steven M.; Heiser, Laura; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K.; Carlin, Daniel E.; Zhang, Yang; Sokolov, Artem; Paull, Evan O.; Wong, Chris K.; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V.; Favorov, Alexander V.; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W.; Long, Byron L.; Noren, David P.; Bisberg, Alexander J.; Mills, Gordon; Gray, Joe; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A.; Fertig, Elana J.; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M.; Spellman, Paul; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach.

In: Nature Methods, Vol. 13, No. 4, 30.03.2016, p. 310-318.

Research output: Contribution to journalArticle

Hill, SM, Heiser, L, Cokelaer, T, Unger, M, Nesser, NK, Carlin, DE, Zhang, Y, Sokolov, A, Paull, EO, Wong, CK, Graim, K, Bivol, A, Wang, H, Zhu, F, Afsari, B, Danilova, LV, Favorov, AV, Lee, WS, Taylor, D, Hu, CW, Long, BL, Noren, DP, Bisberg, AJ, Mills, G, Gray, J, Kellen, M, Norman, T, Friend, S, Qutub, AA, Fertig, EJ, Guan, Y, Song, M, Stuart, JM, Spellman, P, Koeppl, H, Stolovitzky, G, Saez-Rodriguez, J & Mukherjee, S 2016, 'Inferring causal molecular networks: Empirical assessment through a community-based effort', Nature Methods, vol. 13, no. 4, pp. 310-318. https://doi.org/10.1038/nmeth.3773
Hill, Steven M. ; Heiser, Laura ; Cokelaer, Thomas ; Unger, Michael ; Nesser, Nicole K. ; Carlin, Daniel E. ; Zhang, Yang ; Sokolov, Artem ; Paull, Evan O. ; Wong, Chris K. ; Graim, Kiley ; Bivol, Adrian ; Wang, Haizhou ; Zhu, Fan ; Afsari, Bahman ; Danilova, Ludmila V. ; Favorov, Alexander V. ; Lee, Wai Shing ; Taylor, Dane ; Hu, Chenyue W. ; Long, Byron L. ; Noren, David P. ; Bisberg, Alexander J. ; Mills, Gordon ; Gray, Joe ; Kellen, Michael ; Norman, Thea ; Friend, Stephen ; Qutub, Amina A. ; Fertig, Elana J. ; Guan, Yuanfang ; Song, Mingzhou ; Stuart, Joshua M. ; Spellman, Paul ; Koeppl, Heinz ; Stolovitzky, Gustavo ; Saez-Rodriguez, Julio ; Mukherjee, Sach. / Inferring causal molecular networks : Empirical assessment through a community-based effort. In: Nature Methods. 2016 ; Vol. 13, No. 4. pp. 310-318.
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