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Voting-based integration algorithm improves causal network learning from interventional and observational data: An application to cell signaling network inference
In order to increase statistical power for learning a causal network, data are often pooled from multiple observational and interventional experiments. However, if the direct effects of interventions are uncertain, multi-experiment data pooling can result in false causal discoveries. We present a ne...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7869988/ https://www.ncbi.nlm.nih.gov/pubmed/33556096 http://dx.doi.org/10.1371/journal.pone.0245776 |