TRENDY: gene regulatory network inference enhanced by transformer.

ABSTRACT:

Motivation

Gene regulatory networks (GRNs) play a crucial role in the control of cellular functions. Numerous methods have been developed to infer GRNs from gene expression data, including mechanism-based approaches, information-based approaches, and more recent deep learning techniques, the last of which often overlook the underlying gene expression mechanisms.

Results

In this work, we introduce TRENDY, a novel GRN inference method that integrates transformer models to enhance the mechanism-based WENDY approach. Through testing on both simulated and experimental datasets, TRENDY demonstrates superior performance compared to existing methods. Furthermore, we apply this transformer-based approach to three additional inference methods, showcasing its broad potential to enhance

SUBMITTER: Tian X 

PROVIDER: S-EPMC12133277 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature