Predicting Pseudomonas aeruginosa drug resistance using artificial intelligence and whole genome sequencing

Pseudomonas aeruginosa is a key bacterial pathogen that causes significant global morbidity and mortality. Antimicrobial resistance (AMR) emerges rapidly in P. aeruginosa and negatively impacts patient outcomes. Predicting AMR in P. aeruginosa using whole genome sequencing (WGS) and rule-based approaches has been difficult due to the complexity of AMR mechanisms. We hypothesized that using WGS with machine learning approaches could successfully predict AMR in P. aeruginosa.