Leveraging large language models to predict antibiotic resistance in Mycobacterium tuberculosis.

ABSTRACT:

Motivation

Antibiotic resistance in Mycobacterium tuberculosis (MTB) poses a significant challenge to global public health. Rapid and accurate prediction of antibiotic resistance can inform treatment strategies and mitigate the spread of resistant strains. In this study, we present a novel approach leveraging large language models (LLMs) to predict antibiotic resistance in MTB (LLMTB). Our model is trained and evaluated on genomic data from 12 185 CRyPTIC isolates and their associated resistance profiles, utilizing natural language processing techniques to capture patterns and mutations linked to resistance. The model's architecture integrates state-of-the-art transformer-based LLMs, enabling the analysis of complex genomic sequences and the extraction of critical features relevant

SUBMITTER: Testagrose C 

PROVIDER: S-EPMC12261485 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature