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

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dc.citation.firstpage310
dc.citation.journalTitleNature Methods
dc.citation.lastpage318
dc.citation.volumeNumber13
dc.contributor.authorHill, Steven M.
dc.contributor.authorHeiser, Laura M.
dc.contributor.authorCokelaer, Thomas
dc.contributor.authorUnger, Michael
dc.contributor.authorNesser, Nicole K.
dc.contributor.authorCarlin, Daniel E.
dc.contributor.authorZhang, Yang
dc.contributor.authorSokolov, Artem
dc.contributor.authorPaull, Evan O.
dc.contributor.authorWong, Chris K.
dc.contributor.authorGraim, Kiley
dc.contributor.authorBivol, Adrian
dc.contributor.authorWang, Haizhou
dc.contributor.authorZhu, Fan
dc.contributor.authorAfsari, Bahman
dc.contributor.authorDanilova, Ludmila V.
dc.contributor.authorFavorov, Alexander V.
dc.contributor.authorLee, Wai Shing
dc.contributor.authorTaylor, Dane
dc.contributor.authorHu, Chenyue W.
dc.contributor.authorLong, Byron L.
dc.contributor.authorNoren, David P.
dc.contributor.authorBisberg, Alexander J.
dc.contributor.authorHPN-DREAM Consortium
dc.contributor.authorMills, Gordon B.
dc.contributor.authorGray, Joe W.
dc.contributor.authorKellen, Michael
dc.contributor.authorNorman, Thea
dc.contributor.authorFriend, Stephen
dc.contributor.authorQutub, Amina A.
dc.contributor.authorFertig, Elana J.
dc.contributor.authorGuan, Yuanfang
dc.contributor.authorSong, Mingzhou
dc.contributor.authorStuart, Joshua M.
dc.contributor.authorSpellman, Paul T.
dc.contributor.authorKoeppl, Heinz
dc.contributor.authorStolovitzky, Gustavo
dc.contributor.authorSaez-Rodriguez, Julio
dc.contributor.authorMukherjee, Sach
dc.date.accessioned2017-05-05T19:00:54Z
dc.date.available2017-05-05T19:00:54Z
dc.date.issued2016
dc.description.abstractIt 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.identifier.citationHill, Steven M., Heiser, Laura M., Cokelaer, Thomas, et al.. "Inferring causal molecular networks: empirical assessment through a community-based effort." <i>Nature Methods,</i> 13, (2016) Springer Nature: 310-318. https://doi.org/10.1038/nmeth.3773.
dc.identifier.doihttps://doi.org/10.1038/nmeth.3773
dc.identifier.urihttps://hdl.handle.net/1911/94203
dc.language.isoeng
dc.publisherSpringer Nature
dc.rightsThis 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.titleInferring causal molecular networks: empirical assessment through a community-based effort
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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