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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...

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Detalles Bibliográficos
Autores principales: Sinha, Meghamala, Tadepalli, Prasad, Ramsey, Stephen A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
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
Descripción
Sumario: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 new method, “Learn and Vote,” for inferring causal interactions from multi-experiment datasets. In our method, experiment-specific networks are learned from the data and then combined by weighted averaging to construct a consensus network. Through empirical studies on synthetic and real-world datasets, we found that for most of the larger-sized network datasets that we analyzed, our method is more accurate than state-of-the-art network inference approaches.