Machine learning-based approach for identification of new resistance associated mutations from whole genome sequences of <i>Mycobacterium tuberculosis</i>.

ABSTRACT:

Motivation

Currently available methods for the prediction of genotypic drug resistance in Mycobacterium tuberculosis utilize information on known markers of drug resistance. Hence, machine learning approaches are needed that can discover new resistance markers.

Results

Whole genome sequences with known phenotypic drug resistance profiles have been utilized to train XGBoost and ANN classifiers for 5 first-line and 8 second-line tuberculosis drugs. Benchmarking on a completely independent dataset from CRyPTIC database revealed that our method has high sensitivity (90%-95%) and specificity (94%-99%) for five first-line drugs and robust performance for six second-line drugs with a sensitivity of 77%-89% at over 95% specificity. An explainable AI method, SHapley Additive e

SUBMITTER: Pal A 

PROVIDER: S-EPMC11930343 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature