What question did this study set out to answer?

To develop a Bayesian method for inferring edge directions in directed acyclic graphs while allowing for flexible priors based on biological knowledge.

March 18, 2026PLoS Computational BiologyOpen Access

Approximate Bayesian inference of directed acyclic graphs in biology with flexible priors on edge states

View Full Paper

Ask AI

Bookmark

To develop a Bayesian method for inferring edge directions in directed acyclic graphs while allowing for flexible priors based on biological knowledge.
Introduced baycn for approximate Bayesian inference of graph structures.
Used a Metropolis-Hastings-like sampler to infer edge probabilities.
Incorporated user-specified priors to handle sparsity and input biological graphs.
Applied to genomic data for target gene distinction and transcription factor binding.
Baycn demonstrates improved accuracy in inferring directed edges and their absence.
Successfully distinguishes target genes influenced by a shared genetic variant.
Infers effective combinatorial binding of transcription factors, showing enhanced modeling of complex interactions.

Discussion

No takes yet. Share an insight, caveat, or question.

Implication

Bayesian inference method improves edge accuracy in biological systems, suggesting a way to better model complex interactions.

Cite This Study

Martin et al. (2026) studied this question.

synapsesocial.com/papers/69ba43a84e9516ffd37a5217 https://doi.org/10.1371/journal.pcbi.1014039

View Full Paper

Ask AI

Bookmark

Approximate Bayesian inference of directed acyclic graphs in biology with flexible priors on edge states

Key Points

Discussion

Implication

Cite This Study

Approximate Bayesian inference of directed acyclic graphs in biology with flexible priors on edge states

Key Points

Discussion

Implication

Cite This Study

Also Consider

Also Consider