Alicia Pageau

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.

The paper shows that resistance mutations to antifungal drugs are conserved across fungal species but epistasis limits their transferability.

This paper trains a machine learning model to predict antifolate resistance mutations in Pneumocystis jirovecii using experimental data and computational modeling, finding mutations that confer resistance to trimethoprim-sulfamethoxazole.

The paper identifies key residues in Fks hotspots that confer echinocandin resistance in Saccharomyces cerevisiae using molecular dynamics and deep-mutational scanning.

gyoza is a Snakemake workflow that simplifies the analysis of deep-mutational scanning data into functional impact scores with quality control and reporting.

The paper investigates how Erg3 loss-of-function contributes to echinocandin resistance in Candida species by analyzing combined mutations in ERG3 and FKS2.

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 presents a cost-effective method for constructing barcoded libraries for genes of any length using low-cost DNA synthesis and standard cloning technologies.

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

The paper shows that three lateral hypothalamic outputs coordinate to control motivated behavior in mice by modulating downstream neural activity.