Charmie K. Vang

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.

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.