TitleVoting-based integration algorithm improves causal network learning from interventional and observational data: An application to cell signaling network inference.
Publication TypeJournal Article
Year of Publication2021
AuthorsSinha, M, Tadepalli, P, Ramsey, SA
JournalPLoS One
Date Published2021

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.

Alternate JournalPLoS One
PubMed ID33556096
PubMed Central IDPMC7869988
Grant ListOT2 TR002520 / TR / NCATS NIH HHS / United States