Cameron R. Lamoureux

The paper introduces a high-throughput rLC-MS system for metabolomics analysis, demonstrating its ability to capture metabolic variation in large human populations and predict metabolic aging.

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 predict E. coli transcriptional regulatory networks from DNA sequences, finding that motif strength and DNA shape are key predictors.

The paper uses iModulon analysis to uncover the transcriptional regulatory network of Pseudomonas syringae across diverse conditions and interactions.

The paper uses adaptive laboratory evolution and transcriptomic analysis to identify six mechanisms of paraquat tolerance in E. coli.

The paper evolves ETS variants in E. coli and finds they all optimize growth rate and proteome allocation, with conserved aero-type strategies except for ETS-4H.

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

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

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

The paper constructs a transcriptional regulatory network for Escherichia coli using machine learning on a large RNA-seq compendium, identifying regulatory modules and novel regulons.