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dc.contributor.authorTriantafillou, Sofia
dc.contributor.authorLagani, Vincenzo
dc.contributor.authorHeinze-Deml, Christina
dc.contributor.authorSchmidt, Angelika
dc.contributor.authorTegner, Jesper
dc.contributor.authorTsamardinos, Ioannis
dc.date.accessioned2017-10-09T09:03:13Z
dc.date.available2017-10-09T09:03:13Z
dc.date.issued2017-10-05
dc.identifier.citationTriantafillou S, Lagani V, Heinze-Deml C, Schmidt A, Tegner J, et al. (2017) Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells. Scientific Reports 7. Available: http://dx.doi.org/10.1038/s41598-017-08582-x.
dc.identifier.issn2045-2322
dc.identifier.pmid28983114
dc.identifier.doi10.1038/s41598-017-08582-x
dc.identifier.urihttp://hdl.handle.net/10754/625840
dc.description.abstractLearning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated  causal discovery from limited experiments exist, but have so far rarely been tested in systems biology applications. In this work, we apply state-of-the art causal discovery methods on a large collection of public mass cytometry data sets, measuring intra-cellular signaling proteins of the human immune system and their response to several perturbations. We show how different experimental conditions can be used to facilitate causal discovery, and apply two fundamental methods that produce context-specific causal predictions. Causal predictions were reproducible across independent data sets from two different studies, but often disagree with the KEGG pathway databases. Within this context, we discuss the caveats we need to overcome for automated causal discovery to become a part of the routine data analysis in systems biology.
dc.description.sponsorshipThe authors would like to thank Karen Sachs for offering valuable insights on mass cytometry, and Joris Mooij for helpful discussions. S.T., V.L. and I.T. were supported by the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement n. 617393.
dc.publisherSpringer Nature
dc.relation.urlhttps://www.nature.com/articles/s41598-017-08582-x
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titlePredicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells
dc.typeArticle
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentBioscience Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalScientific Reports
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Computer Science, University of Crete, Rethimno, Greece.
dc.contributor.institutionDepartment of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA.
dc.contributor.institutionSeminar for Statistics, ETH Zurich, Zurich, Switzerland.
dc.contributor.institutionUnit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska Institutet & Karolinska University Hospital, Science for Life Laboratory, Stockholm, Sweden.
kaust.personTegner, Jesper
refterms.dateFOA2018-06-13T17:57:37Z
dc.date.published-online2017-10-05
dc.date.published-print2017-12


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This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Except where otherwise noted, this item's license is described as This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.