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 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.Author summary: In this study, we have developed a faster and scalable method, the Fast Greedy Equivalence Search (FGES)-Merge, to understand how genes interact and regulate each other. We adapted it specifically for massive gene regulatory networks, which can have tens of thousands of genes. Our method is not only competitive with the current best methods in terms of accuracy but also outperforms them in terms of speed. This is crucial when working with large scale data such as the human genome.