Reverse-Engineering Gene Regulatory Networks with Bayesian Inference

Reverse-engineering gene regulatory networks (GRNs) with Bayesian inference is a probabilistic approach to uncover how genes influence one another from noisy biological data. Instead of producing a single “best” network, Bayesian methods use prior knowledge and observed data to generate a distribution of possible networks, assigning probabilities to each potential regulatory interaction. This allows researchers to quantify uncertainty, integrate biological priors, and highlight the most likely gene-gene connections. While powerful for handling sparse, high-dimensional datasets, Bayesian inference is computationally demanding and often constrained by assumptions like acyclic network structures.