Abstract
Antimicrobials against parasitic pathogens have transformed human and animal health. Their use (and misuse) has led to the emergence of antimicrobial resistance (AMR) that poses potentially catastrophic threat to public health. Sequencing offers powerful ways to respond to the development of AMR. To leverage this information, sophisticated machine learning approaches are required to identify potential resistance mutations. This project aims to develop a framework to understand the molecular consequences of variation allowing for the anticipation of resistance mutations before they become fixed in a population. It will have a direct impact in surveillance, stewardship and the development of new interventions.
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