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...

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Detalles Bibliográficos
Autores principales: Triantafillou, Sofia, Lagani, Vincenzo, Heinze-Deml, Christina, Schmidt, Angelika, Tegner, Jesper, Tsamardinos, Ioannis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5629212/
https://www.ncbi.nlm.nih.gov/pubmed/28983114
http://dx.doi.org/10.1038/s41598-017-08582-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5629212/
https://www.ncbi.nlm.nih.gov/pubmed/28983114
http://dx.doi.org/10.1038/s41598-017-08582-x

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