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

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dc.citation.firstpage310en_US
dc.citation.journalTitleNature Methodsen_US
dc.citation.lastpage318en_US
dc.citation.volumeNumber13en_US
dc.contributor.authorHill, Steven M.en_US
dc.contributor.authorHeiser, Laura M.en_US
dc.contributor.authorCokelaer, Thomasen_US
dc.contributor.authorUnger, Michaelen_US
dc.contributor.authorNesser, Nicole K.en_US
dc.contributor.authorCarlin, Daniel E.en_US
dc.contributor.authorZhang, Yangen_US
dc.contributor.authorSokolov, Artemen_US
dc.contributor.authorPaull, Evan O.en_US
dc.contributor.authorWong, Chris K.en_US
dc.contributor.authorGraim, Kileyen_US
dc.contributor.authorBivol, Adrianen_US
dc.contributor.authorWang, Haizhouen_US
dc.contributor.authorZhu, Fanen_US
dc.contributor.authorAfsari, Bahmanen_US
dc.contributor.authorDanilova, Ludmila V.en_US
dc.contributor.authorFavorov, Alexander V.en_US
dc.contributor.authorLee, Wai Shingen_US
dc.contributor.authorTaylor, Daneen_US
dc.contributor.authorHu, Chenyue W.en_US
dc.contributor.authorLong, Byron L.en_US
dc.contributor.authorNoren, David P.en_US
dc.contributor.authorBisberg, Alexander J.en_US
dc.contributor.authorHPN-DREAM Consortiumen_US
dc.contributor.authorMills, Gordon B.en_US
dc.contributor.authorGray, Joe W.en_US
dc.contributor.authorKellen, Michaelen_US
dc.contributor.authorNorman, Theaen_US
dc.contributor.authorFriend, Stephenen_US
dc.contributor.authorQutub, Amina A.en_US
dc.contributor.authorFertig, Elana J.en_US
dc.contributor.authorGuan, Yuanfangen_US
dc.contributor.authorSong, Mingzhouen_US
dc.contributor.authorStuart, Joshua M.en_US
dc.contributor.authorSpellman, Paul T.en_US
dc.contributor.authorKoeppl, Heinzen_US
dc.contributor.authorStolovitzky, Gustavoen_US
dc.contributor.authorSaez-Rodriguez, Julioen_US
dc.contributor.authorMukherjee, Sachen_US
dc.date.accessioned2017-05-05T19:00:54Zen_US
dc.date.available2017-05-05T19:00:54Zen_US
dc.date.issued2016en_US
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.en_US
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.en_US
dc.identifier.doihttps://doi.org/10.1038/nmeth.3773en_US
dc.identifier.urihttps://hdl.handle.net/1911/94203en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
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.ﾠen_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/3.0/en_US
dc.titleInferring causal molecular networks: empirical assessment through a community-based efforten_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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