Predicting Drug Resistance in <i>Mycobacterium tuberculosis</i>: A Machine Learning Approach to Genomic Mutation Analysis.

ABSTRACT: Background/Objectives: Tuberculosis (TB), caused by Mycobacterium tuberculosis (M. tuberculosis), remains a leading cause of death from infectious diseases globally. The treatment of active TB relies on first- and second-line drugs, however, the emergence of drug resistance poses a significant challenge to global TB control efforts. Recent advances in whole-genome sequencing combined with machine learning have shown promise in predicting drug resistance. This study aimed to evaluate the performance of four machine learning models in classifying resistance to ethambutol, isoniazid, and rifampicin in M. tuberculosis isolates. Methods: Four machine learning models-Extreme Gradient Boosting Classifier (XGBC), Logistic Gradient Boosting Classifier (LGBC), Grad

SUBMITTER: Paredes-Gutierrez G 

PROVIDER: S-EPMC11817661 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature