Marcus Nguyen

This study builds machine learning classifiers to predict variable gene presence in E. coli genomes, achieving high accuracy for most gene families, including poorly annotated and horizontally transferred genes.

This paper develops a random forest model to predict plasmid hosts, achieving 0.662 and 0.867 Matthews correlation coefficients at species and order levels, respectively.

The paper sequences 320 SARS-CoV-2 genomes from Houston to study early pandemic spread, finding diverse origins and no evidence of more virulent strains.

This study shows that machine learning can predict antimicrobial resistance using conserved non-AMR genes from incomplete genomes, achieving F1 scores of 0.80-0.89 with 100 genes.

This paper uses machine learning to predict the virulence of Pseudomonas aeruginosa isolates based on genomic data, achieving 75% accuracy using random forest models.

This study uses XGBoost to predict antimicrobial MICs for nontyphoidal Salmonella with high accuracy using genomic data.

This study develops an XGBoost-based machine learning model to predict minimum inhibitory concentrations for 20 antibiotics in Klebsiella pneumoniae using whole genome sequencing data, achieving 92% overall accuracy.