GTAT-GRN: a graph topology-aware attention method with multi-source feature fusion for gene regulatory network inference.

ABSTRACT: Gene regulatory network (GRN) inference is a central task in systems biology. However, due to the noisy nature of gene expression data and the diversity of regulatory structures, accurate GRN inference remains challenging. We hypothesize that integrating multi-source features and leveraging an attention mechanism that explicitly captures graph structure can enhance GRN inference performance. Based on this, we propose GTAT-GRN, a deep graph neural network model with a graph topological attention mechanism that fuses multi-source features. GTAT-GRN includes a feature fusion module to jointly model temporal expression patterns, baseline expression levels, and structural topological attributes, improving node representation. In addition, we introduce the Graph Topology-Aware Attention Network

SUBMITTER: Wang S 

PROVIDER: S-EPMC12540167 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature