Voting-based integration algorithm improves causal network learning from interventional and observational data: An application to cell signaling network inference

Meghamala Sinha, Prasad Tadepalli and Stephen A Ramsey

PLOS ONE, 2021, vol. 16, issue 2, 1-18

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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0245776

DOI: 10.1371/journal.pone.0245776

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