Alison S. Low

This paper develops a machine learning model that integrates bacterial and viral genomic data to predict phage activity against E. coli, improving prediction accuracy by incorporating phage gene content.

This study benchmarks in silico AMR prediction tools using nanopore data, finding that assembly strategy and data volume impact accuracy, but no tool perfectly matches lab-based AST results.

This paper evaluates the use of an endotoxin-reduced E. coli strain for phage production, showing it can generate diverse phage with low endotoxin and minimal downstream processing.

This study identifies mechanisms by which E. coli ST131 evades phage killing through quorum sensing and capsule expression, and develops a phage therapy strategy to overcome these defenses in urine-based conditions.

This paper uses machine learning to predict effective phage cocktails for treating E. coli urinary tract infections based on bacterial genome data.