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

Hill, S. M. ; Heiser, L. M. ; Cokalaer, T. ; Unger, M. ; Nesser, N. K. ; Carlin, D. E. ; Zhang, Y. ; Sokolov, A. ; Paull, E. O. ; Wong, C. K. ; Graim, K. ; Bivol, A. ; Wang, H. ; Zhu, F. ; Afsari, B. ; Danilova, L. V. ; Favorov, A. V. ; Lee, W. S. ; Taylor, D. ; Hu, C. W. ; Long, B. L. ; Noren, D. P. ; Bisberg, A. J. ; Mills, G. B. ; Gray, J. W. ; Kellen, M. ; Norman, T. ; Friend, S. ; Qutub, A. A. ; Fertig, E. J. ; Guan, Y. ; Song, M. ; Stuart, J. M. ; Spellman, P. T. ; Koeppl, H. ; Stolovitzky, G. ; Saez-Rodriguez, J. ; Mukherjee, S. (2016):
Interferring causal molecular networks: empirical assessment through a community-based effort.
In: Nature methods, 13 (4), pp. 310-318. Nature Publishing Group, e-ISSN 1548-7105,
[Article]

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-DREDREDREAM 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.

Item Type: Article
Erschienen: 2016
Creators: Hill, S. M. ; Heiser, L. M. ; Cokalaer, T. ; Unger, M. ; Nesser, N. K. ; Carlin, D. E. ; Zhang, Y. ; Sokolov, A. ; Paull, E. O. ; Wong, C. K. ; Graim, K. ; Bivol, A. ; Wang, H. ; Zhu, F. ; Afsari, B. ; Danilova, L. V. ; Favorov, A. V. ; Lee, W. S. ; Taylor, D. ; Hu, C. W. ; Long, B. L. ; Noren, D. P. ; Bisberg, A. J. ; Mills, G. B. ; Gray, J. W. ; Kellen, M. ; Norman, T. ; Friend, S. ; Qutub, A. A. ; Fertig, E. J. ; Guan, Y. ; Song, M. ; Stuart, J. M. ; Spellman, P. T. ; Koeppl, H. ; Stolovitzky, G. ; Saez-Rodriguez, J. ; Mukherjee, S.
Title: Interferring causal molecular networks: empirical assessment through a community-based effort
Language: English
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-DREDREDREAM 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.

Journal or Publication Title: Nature methods
Journal volume: 13
Number: 4
Publisher: Nature Publishing Group
Uncontrolled Keywords: Cancer models, systems biology, Cellular signalling networks
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
Date Deposited: 24 Feb 2016 11:45
Official URL: http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth...
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