James E. Posey

This paper introduces a 3D tuberculoma bioplatform for studying tuberculosis biology and high-content screening, showing it can generate structured lesions and be scaled for high-throughput use.

This paper develops a 3D bioplatform to screen host-directed therapeutics for tuberculosis, identifying compounds that reduce mycobacterial burdens and granuloma lesions.

GenomegaMap estimates genome-wide dN/dS ratios across 10,209 tuberculosis genomes to identify selection pressures shaping the pathogen's evolution.

This paper predicts pyrazinamide resistance in Mycobacterium tuberculosis using machine learning models trained on pncA mutations and structural features, achieving 80.2% sensitivity and 76.9% specificity.