Mahbuba Tasmin

The paper predicts antibiotic resistance mutations in Mycobacterium tuberculosis by integrating structural context with mutation data, finding that structural clustering improves prediction accuracy.

The paper develops FARM, a framework to predict antibiotic resistance in Mycobacterium tuberculosis by combining biophysical and machine learning features, achieving high accuracy in distinguishing resistance-associated variants.

BIG-TB evaluates sequence-based machine learning models for predicting antibiotic resistance and variant attribution in Mycobacterium tuberculosis genomes, showing DNA-based models do not outperform simpler baselines.