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Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells

Learning 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 exi...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Veröffentlicht in:Sci Rep
Hauptverfasser: Triantafillou, Sofia, Lagani, Vincenzo, Heinze-Deml, Christina, Schmidt, Angelika, Tegner, Jesper, Tsamardinos, Ioannis
Format: Artigo
Sprache:Inglês
Veröffentlicht: Nature Publishing Group UK 2017
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Online Zugang:https://ncbi.nlm.nih.gov/pmc/articles/PMC5629212/
https://ncbi.nlm.nih.gov/pubmed/28983114
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41598-017-08582-x
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