AttentionGRN: a functional and directed graph transformer for gene regulatory network reconstruction from scRNA-seq data.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) enables the reconstruction of cell type-specific gene regulatory networks (GRNs), offering detailed insights into gene regulation at high resolution. While graph neural networks have become widely used for GRN inference, their message-passing mechanisms are often limited by issues such as over-smoothing and over-squashing, which hinder the preservation of essential network structure. To address these challenges, we propose a novel graph transformer-based model, AttentionGRN, which leverages soft encoding to enhance model expressiveness and improve the accuracy of GRN inference from scRNA-seq data. Furthermore, the GRN-oriented message aggregation strategies are designed to capture both the directed network structure information and functional informat

SUBMITTER: Gao Z 

PROVIDER: S-EPMC11926986 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature