Reo Yoo

Mining all publicly available expression data to compute dynamic microbial transcriptional regulatory networks

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

Pan-genomic analysis of transcriptional modules across Salmonella Typhimurium reveals the regulatory landscape of different strains

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.

Machine learning of all Mycobacterium Tuberculosis H37Rv RNA-seq data reveals complex interplay between metabolism, stress response, and infection

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

Machine learning uncovers a data-driven transcriptional regulatory network for the Crenarchaeal thermoacidophile Sulfolobus acidocaldarius

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