Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian networks.

ABSTRACT: We present the Fast Greedy Equivalence Search (FGES)-Merge, a new method for learning the structure of gene regulatory networks via merging locally learned Bayesian networks, based on the fast greedy equivalent search algorithm. The method is competitive with the state of the art in terms of the Matthews correlation coefficient, which takes into account both precision and recall, while also improving upon it in terms of speed, scaling up to tens of thousands of variables and being able to use empirical knowledge about the topological structure of gene regulatory networks. To showcase the ability of our method to scale to massive networks, we apply it to learning the gene regulatory network for the full human genome using data from samples of different brain structures (from the Allen Human

SUBMITTER: Bernaola N 

PROVIDER: S-EPMC10745139 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature