Minimal gene signatures enable high-accuracy prediction of antibiotic resistance in Pseudomonas aeruginosa.

ABSTRACT: Antimicrobial resistance (AMR) in Pseudomonas aeruginosa poses a critical global health challenge, with current diagnostics relying on slow, culture-based methods. Here, we present a ML framework leveraging transcriptomic data to predict antibiotic resistance with high accuracy. We applied a genetic algorithm to 414 clinical isolates to identify minimal, highly predictive gene sets (~35-40 genes) distinguishing resistant from susceptible strains for meropenem, ciprofloxacin, tobramycin, and ceftazidime. Automated ML classifiers trained on these sets achieved accuracies of 96-99% on test data (F1 scores: 0.93-0.99), surpassing clinical deployment thresholds. Multiple distinct, non-overlapping gene subsets exhibited comparable performance, suggesting that resistance acquisition is associated

SUBMITTER: Shahreen N 

PROVIDER: S-EPMC12491527 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature