Exploring the Role of Transposons in Predicting Antimicrobial Resistance in Tuberculosis and Its Co-infections Using Explainable Machine Learning

Abstract

Antimicrobial resistance (AMR) poses a growing global health threat, particularly in the treatment of tuberculosis (TB) and its common co-infections such as Klebsiella pneumoniae and Staphylococcus aureus. Traditional antimicrobial susceptibility testing (AST), while accurate, is time-intensive and inaccessible in many clinical settings. This study presents a genome-based machine learning (ML) framework that integrates AMR gene and transposon detection to enhance the prediction of drug resistance. Using whole-genome sequences (WGS) sourced from NCBI, transposons were identified via TnComp finder and AMR genes via ABRicate. Feature engineering focused on transposon-AMR gene co-occurrence, and five supervised ML models—Logistic Regression, Random Forest, XGBoost, AdaBoost, and Support Vector Machine—were trained and evaluated with and without SMOTE oversampling. Model performance was assessed using accuracy, precision, recall, AUC, and F2-score, with top-performing models achieving AUC scores above 0.85 and F2-scores above 0.80 for several antibiotics. Explainability was introduced through feature importance analysis, highlighting key AMR gene-transposon interactions influencing resistance. A web application, Resist- Gen, was developed to operationalize this pipeline, enabling users to input WGS data in FASTA format and obtain rapid resistance predictions. This approach underscores the significant role of transposons in AMR dissemination and offers a scalable, interpretable, and clinically relevant tool for guiding antibiotic treatment strategies.