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

Hill, S. M. and Heiser, L. M. and Cokalaer, T. and Unger, M. and Nesser, N. K. and Carlin, D. E. and Zhang, Y. and Sokolov, A. and Paull, E. O. and Wong, C. K. and Graim, K. and Bivol, A. and Wang, H. and Zhu, F. and Afsari, B. and Danilova, L. V. and Favorov, A. V. and Lee, W. S. and Taylor, D. and Hu, C. W. and Long, B. L. and Noren, D. P. and Bisberg, A. J. and Mills, G. B. and Gray, J. W. and Kellen, M. and Norman, T. and Friend, S. and Qutub, A. A. and Fertig, E. J. and Guan, Y. and Song, M. and Stuart, J. M. and Spellman, P. T. and Koeppl, H. and Stolovitzky, G. and Saez-Rodriguez, J. and Mukherjee, S. (2016):
Interferring causal molecular networks: empirical assessment through a community-based effort.
In: Nature methods, Nature Publishing Group, [Online-Edition: http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth...],
[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. and Heiser, L. M. and Cokalaer, T. and Unger, M. and Nesser, N. K. and Carlin, D. E. and Zhang, Y. and Sokolov, A. and Paull, E. O. and Wong, C. K. and Graim, K. and Bivol, A. and Wang, H. and Zhu, F. and Afsari, B. and Danilova, L. V. and Favorov, A. V. and Lee, W. S. and Taylor, D. and Hu, C. W. and Long, B. L. and Noren, D. P. and Bisberg, A. J. and Mills, G. B. and Gray, J. W. and Kellen, M. and Norman, T. and Friend, S. and Qutub, A. A. and Fertig, E. J. and Guan, Y. and Song, M. and Stuart, J. M. and Spellman, P. T. and Koeppl, H. and Stolovitzky, G. and Saez-Rodriguez, J. and 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
Publisher: Nature Publishing Group
Uncontrolled Keywords: Cancer models, systems biology, Cellular signalling networks
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology
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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