ArticlePLoS computational biology2023
Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian networks.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Siibra: a software tool suite for realizing a Multilevel Human Brain Atlas from complex data resources.Nature methods · 2026Article
- Bayesian networks for network inference in biology.Journal of the Royal Society, Interface · 2025Review
- A Novel Hyper-Heuristic Algorithm with Soft and Hard Constraints for Causal Discovery Using a Linear Structural Equation Model.Entropy (Basel, Switzerland) · 2025Article
- Explainable artificial intelligence for omics data: a systematic mapping study.Briefings in bioinformatics · 2023Review
- NeuroSuites: An online platform for running neuroscience, statistical, and machine learning tools.Frontiers in neuroinformatics · 2023Article
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Authors and funding
4 authors.
Funding
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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 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.
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Registered trials
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