Predicting Causal Relationships from Biological Data: Applying Automated Causal Discovery on Mass Cytometry Data of Human Immune Cells
Abstract 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 experi...
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| Auteurs principaux: | , , , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Nature Portfolio
2017
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/ec82a6e1b2fd40ee8b1e4ad2d4552b47 |
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