Efficient structure learning of gene regulatory networks with Bayesian active learning.
Journal:
BMC bioinformatics
Published Date:
Jun 3, 2025
Abstract
BACKGROUND: Gene regulatory network modeling is a complex structure learning problem that involves both observational data analysis and experimental interventions. Bayesian causal discovery provides a principled framework for modeling observational data, generating posterior distributions that best represent the underlying structure. While recent algorithms offer efficient and accurate structure learning, integrating experiment design can further enhance predictive performance.