Camille Bédard

This study uses population genomics and machine learning to identify genetic markers predictive of antifungal resistance in Candida albicans, highlighting the need for expanded resistance catalogues.

This paper develops a heterologous expression system in yeast to rapidly test and discover resistance-conferring mutations from multiple pathogenic fungi.

The paper curates AMR mutations in fungi to create FungAMR, identifies universal resistance mutations, and provides a tool to screen genomes for AMR mutations.

The paper evaluates 4,000 Erg11 variants to determine azole resistance and fitness costs, finding 33% resistance, 88% cross-resistance, and minimal fitness cost.