GT-GRN: a graph transformer framework for enhanced gene regulatory network inference via multimodal embedding of expression data and existing network knowledge.

ABSTRACT: The inference of gene regulatory networks (GRNs) is critical for understanding the regulatory mechanisms underlying cellular development, functional specialization, and disease progression. Predicting regulatory gene interactions-often framed as a link prediction task-is a foundational step toward modeling cellular behavior. However, GRN inference from gene coexpression data alone is limited by noise, low interpretability, and difficulty in capturing indirect regulatory signals. Additionally, challenges such as data sparsity, nonlinearity, and complex gene interactions hinder accurate network reconstruction. To address these issues, we propose, a novel graph transformer (GT) based framework (GT-GRN) that enhances GRN inference by integrating multimodal gene embeddings. Our method combines

SUBMITTER: Teji B 

PROVIDER: S-EPMC12597036 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature