Corrected score methods for estimating Bayesian networks with error‐prone nodes

Motivated by inferring cellular signaling networks using noisy flow cytometry data, we develop procedures to draw inference for Bayesian networks based on error‐prone data. Two methods for inferring causal relationships between nodes in a network are proposed based on penalized estimation methods that account for measurement error and encourage sparsity. We discuss consistency of the proposed network estimators and develop an approach for selecting the tuning parameter in the penalized estimation methods. Empirical studies are carried out to compare the proposed methods with a naive method that ignores measurement error. Finally, we apply these methods to infer signaling networks using single cell flow cytometry data.

• Publication year

  2020

• Venue

  Statistics in Medicine

• Publication date

  2020-02-10

• Fields of study

  Biology, Medicine, Computer Science, Mathematics

• Identifiers
  DOI 10.1002/sim.8925arXiv 2002.03817PMID 33665887
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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