Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda.

ABSTRACT:

Background

Efforts toward tuberculosis management and control are challenged by the emergence of Mycobacterium tuberculosis (MTB) resistance to existing anti-TB drugs. This study aimed to explore the potential of machine learning algorithms in predicting drug resistance of four anti-TB drugs (rifampicin, isoniazid, streptomycin, and ethambutol) in MTB using whole-genome sequence and clinical data from Uganda. We also assessed the model's generalizability on another dataset from South Africa.

Results

We trained ten machine learning algorithms on a dataset comprising of 182 MTB isolates with clinical data variables (age, sex, HIV status) and SNP mutations across the entire genome as predictor variables and phenotypic drug-susceptibility data for the four drugs as the outcome v

SUBMITTER: Babirye SR 

PROVIDER: S-EPMC11622658 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature