Evan Pierce Brenner

The paper introduces amR, an R package suite for predicting antimicrobial resistance in bacterial pathogens using genome data and interpretable machine learning models.

This study identifies genomic signatures of light ecotypes in Prochlorococcus using machine learning and comparative genomics, showing accurate ecotype prediction from partial genomes.

The paper analyzes 125 Pseudomonas aeruginosa isolates from clinical and environmental sources to assess genotype-phenotype correlations and niche specialization, finding that genomic diversity does not correlate with isolation source or most virulence traits, but Group B strains show higher cytotoxicity.

This paper uses machine learning to predict antimicrobial resistance (AMR) across ESKAPE pathogens by analyzing genomic data and identifying molecular features associated with drug resistance.

The paper identifies novel partners and evolutionary history of phage shock protein (PSP) systems across bacteria, showing their presence in LUCA and diversification into new functional contexts.

MolEvolvR provides a web-app for analyzing protein evolution through phylogenetic context, offering tools to explore conserved domains, lineage-specific variants, and domain architectures.