Yuan Yuan

This study identifies a three-phase transcriptional response in E. coli to antibiotic exposure using iModulon analysis, revealing coordinated stress, redox, and metabolic responses.

The paper develops a workflow to reconstruct dynamic transcriptional regulatory networks from public gene expression data using Bacillus subtilis as a case study.

This study uses machine learning to analyze RNA-Seq data from multiple Salmonella Typhimurium strains, revealing transcriptional modules and regulatory patterns across the pan-genome.

The paper uses machine learning to analyze M. tuberculosis RNA-seq data, identifying gene modules and their roles in metabolism, stress response, and infection.

The paper uses machine learning to uncover a transcriptional regulatory network in Sulfolobus acidocaldarius, identifying new gene regulators and regulatory modules.