GAEDGRN: reconstruction of gene regulatory networks based on gravity-inspired graph autoencoders.

ABSTRACT: Reconstructing high-resolution gene regulatory networks (GRNs) based on single-cell RNA sequencing data provides an opportunity to gain insight into disease pathogenesis. At present, there are a large number of GRN reconstruction methods based on graph neural networks, and they can obtain excellent performance in GRN inference by extracting network structure features. However, most of these methods fail to fully exploit the directional characteristics or even ignore them when extracting network structural features. To this end, a novel framework called GAEDGRN is proposed based on gravity-inspired graph autoencoder (GIGAE) to infer potential causal relationships between genes. Among them, GIGAE can help us capture the complex directed network topology in GRN. Additionally, due to the uneve

SUBMITTER: Wei PJ 

PROVIDER: S-EPMC12104625 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature