Revealing hidden regulatory dependencies: multi-perspective graph learning for single-cell gene regulatory network inference.

ABSTRACT: Gene regulatory networks (GRNs) inform analyses of cellular state transitions, regulatory mechanisms, and disease processes. With the rapid development of single-cell sequencing technologies, accurate inference of GRNs from complex and high-dimensional single-cell transcriptomic data remains a core challenge. However, the effective use of multi-level structural and expression features among genes remains a major obstacle to improving inference accuracy. This study presents ATFGRN, an adaptive topology-feature fusion graph neural framework that integrates features from three complementary perspectives for accurate prediction of gene regulatory relationships. The subgraph structure encoding module focuses on local subgraphs of regulatory relationships and identifies structural patterns and t

SUBMITTER: He W 

PROVIDER: S-EPMC12814975 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature