In this webinar, Dr. Geoffrey A. Solano will present two data-driven approaches, genome-based machine learning and network science, to better understand and combat antimicrobial resistance in tuberculosis and its co-infections. By integrating transposon detection and resistance gene network analysis, these frameworks offer scalable tools for improving resistance prediction, surveillance, and public health interventions.