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

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 Mat...

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Bibliographic Details
Published in:PLoS computational biology 2023-12, Vol.19 (12), p.e1011443-e1011443
Main Authors: Bernaola, Niko, Michiels, Mario, Larrañaga, Pedro, Bielza, Concha
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Bayesian analysis
Bayesian statistical decision theory
Brain
Correlation coefficient
Correlation coefficients
Datasets
Equivalence
Explainable artificial intelligence
Genes
Genetic aspects
Genetic regulation
Greedy algorithms
Learning
Mediation
Networks
Physiological aspects
Probability distribution
Search algorithms
Variables
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Summary: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 Brain Atlas). Furthermore, this Bayesian network model should predict interactions between genes in a way that is clear to experts, following the current trends in explainable artificial intelligence. To achieve this, we also present a new open-access visualization tool that facilitates the exploration of massive networks and can aid in finding nodes of interest for experimental tests.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1011443