Details

Autor(en) / Beteiligte

• Sinha, Meghamala
• Tadepalli, Prasad
• Ramsey, Stephen A
• Constantinou, Anthony C

Titel

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

Ist Teil von

• PloS one, 2021-02, Vol.16 (2), p.e0245776-e0245776

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2021

Link zum Volltext

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• 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.