Gene regulatory network prediction using machine learning, deep learning, and hybrid approaches.

ABSTRACT: Construction of gene regulatory networks (GRNs) is essential for elucidating the regulatory mechanisms underlying metabolic pathways, biological processes, and complex traits. In this study, we developed and evaluated machine learning, deep learning, and hybrid approaches for constructing GRNs by integrating prior knowledge and large-scale transcriptomic data from Arabidopsis thaliana, poplar, and maize. Among these, hybrid models that combined convolutional neural networks and machine learning consistently outperformed traditional machine learning and statistical methods, achieving over 95% accuracy on the holdout test datasets. These models not only identified a greater number of known transcription factors regulating the lignin biosynthesis pathway but also demonstrated higher pr

SUBMITTER: Mummadi ST 

PROVIDER: S-EPMC12441907 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature