Saugat Poudel

Interpreting roles of mutations associated with the emergence of S. aureus USA300 strains using transcriptional regulatory network reconstruction

The paper identifies mutations in S. aureus USA300 strains that affect gene regulation by combining genetic association testing with transcriptional regulatory network reconstruction.

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.

Optimal dimensionality selection for independent component analysis of transcriptomic data

This paper introduces OptICA, a method for selecting optimal dimensionality in ICA of transcriptomic data, showing it outperforms existing methods across multiple datasets.

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.

Matrix factorization recovers consistent regulatory signals from disparate datasets

The paper uses matrix factorization to uncover consistent regulatory signals across diverse gene expression datasets, demonstrating its utility in multi-omics analysis.

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning

iModulonDB provides a knowledgebase of microbial transcriptional regulation derived from machine learning, offering curated gene sets and their activity levels across three bacterial species.