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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.accessioned2017-04-10T07:49:51Z
dc.date.available2017-04-10T07:49:51Z
dc.date.issued2017-03-31
dc.identifier.citationTriantafillou S, Lagani V, Heinze-Deml C, Schmidt A, Tegner J, et al. (2017) Predicting Causal Relationships from Biological Data: Applying Automated Casual Discovery on Mass Cytometry Data of Human Immune Cells. Available: http://dx.doi.org/10.1101/122572.
dc.identifier.doi10.1101/122572
dc.identifier.urihttp://hdl.handle.net/10754/623101
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. ST, VL and IT were supported by the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 617393.
dc.publisherCold Spring Harbor Laboratory Press
dc.relation.urlhttp://biorxiv.org/content/early/2017/03/30/122572
dc.rightsThe copyright holder for this preprint is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titlePredicting Causal Relationships from Biological Data: Applying Automated Casual Discovery on Mass Cytometry Data of Human Immune Cells
dc.typePreprint
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.eprint.versionPre-print
dc.contributor.institutionDepartment of Computer Science, University of Crete, Greece
dc.contributor.institutionDepartment of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL
dc.contributor.institutionSeminar for Statistics, ETH 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.dateFOA2018-06-14T02:29:13Z


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The copyright holder for this preprint is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
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