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dc.contributor.authorHill, Steven M.
Heiser, Laura M.
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.
HPN-DREAM Consortium
Mills, Gordon B.
Gray, Joe W.
Kellen, Michael
Norman, Thea
Friend, Stephen
Qutub, Amina A.
Fertig, Elana J.
Guan, Yuanfang
Song, Mingzhou
Stuart, Joshua M.
Spellman, Paul T.
Koeppl, Heinz
Stolovitzky, Gustavo
Saez-Rodriguez, Julio
Mukherjee, Sach
dc.date.accessioned 2017-05-05T19:00:54Z
dc.date.available 2017-05-05T19:00:54Z
dc.date.issued 2016
dc.identifier.citation Hill, Steven M., Heiser, Laura M., Cokelaer, Thomas, et al.. "Inferring causal molecular networks: empirical assessment through a community-based effort." Nature Methods, 13, (2016) Springer Nature: 310-318. https://doi.org/10.1038/nmeth.3773.
dc.identifier.urihttps://hdl.handle.net/1911/94203
dc.description.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 asᅠin silicoᅠdata 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.
dc.language.iso eng
dc.publisher Springer Nature
dc.rights This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material.ᅠ
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/3.0/
dc.title Inferring causal molecular networks: empirical assessment through a community-based effort
dc.type Journal article
dc.citation.journalTitle Nature Methods
dc.citation.volumeNumber 13
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.1038/nmeth.3773
dc.identifier.pmcid PMC4854847
dc.identifier.pmid 26901648
dc.type.publication publisher version
dc.citation.firstpage 310
dc.citation.lastpage 318


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