Robert Olson

The paper compares ARTIC V3 and V4 primer sets for SARS-CoV-2 sequencing, finding improved performance and underrepresentation of G142D substitution in early Delta genomes due to V3 primer limitations.

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

Open-source KBase platform unites reference genomes and biochemistry, over 70 apps, Jupyter-based Narratives with provenance, and a Docker SDK for reproducible plant and microbial systems biology.

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.