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 machine learning (ML) framework leveraging transcriptomic data to predict antibiotic resistance with high accuracy. Using 414 clinical isolates, we applied a genetic algorithm to identify minimal, highly predictive gene sets distinguishing resistant from susceptible strains for meropenem, ciprofloxacin, tobramycin, and ceftazidime. Automated ML classifiers trained on these sets achieved test accuracy of 0.96–0.99, surpassing clinical deployment thresholds. Interestingly, multiple distinct, non-overlapping gene subsets exhibited comparable predictive performance, indicating that resistance acquisition broadly impacts the expression of diverse regulatory and metabolic genes. Comparison with known resistance markers from CARD and operon annotations revealed a substantial number of previously unannotated gene clusters, highlighting significant knowledge gaps in current AMR understanding. Mapping these genes onto independently modulated gene sets (iModulons) revealed transcriptional adaptations occurring across diverse genetic regions. Our analysis identifies previously unexplored transcriptional regulators as promising novel targets, potentially enabling the development of resistance-proof antibiotics.
