Title | Voting-based integration algorithm improves causal network learning from interventional and observational data: An application to cell signaling network inference. |
Publication Type | Journal Article |
Year of Publication | 2021 |
Authors | Sinha, M, Tadepalli, P, Ramsey, SA |
Journal | PLoS One |
Volume | 16 |
Issue | 2 |
Pagination | e0245776 |
Date Published | 2021 |
ISSN | 1932-6203 |
Abstract | 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. |
DOI | 10.1371/journal.pone.0245776 |
Alternate Journal | PLoS One |
PubMed ID | 33556096 |
PubMed Central ID | PMC7869988 |
Grant List | OT2 TR002520 / TR / NCATS NIH HHS / United States |