An explainable machine learning pipeline for prediction of antimicrobial resistance in Pseudomonas aeruginosa

Motivation Prediction of antimicrobial resistance in P. aeruginosa using machine learning and genomic sequences holds the potential to serve as comparable alternatives to laboratory based detection if not better. Additionally, model interpretability can further enhance the potential of these models paving way for their reproducibility. Results We have developed a machine-learning based two tier pipeline to predict resistance phenotype in P.aeruginosa using only genomic sequences as input in the form of k-mers. Our Decision Tree Model yields an accuracy of 79% and AUC-ROC of 0.77 with a 70% specificity and 84% senstivity. We have interpreted the model’s predictions using explainable AI as an attempt to bridge the gap between computational prediction and biological insight. Through these interpretations we have gathered antibiotic specific k-mer signatures pushing phenotype towards resistance.