Austin G. Meyer

The paper uses interpretable machine learning to rank H3N2 hemagglutinin antigenic sites across datasets, improving concordance with prior studies and providing a consolidated reference.

This paper uses explainable machine learning to identify important amino acid changes in Influenza hemagglutinin that correlate with phenotypic changes and host adaptation.

The paper models how AD3 mutations in synaptotagmin 1's C2 domains affect Ca2+ binding pocket dynamics and explains their lethal effects through increased loop mobility and volume changes.

This paper applies a Bayesian framework to analyze infection biomarkers in HIV patients with pneumonia, identifying CRP, PCT, and lytA as significant predictors and demonstrating the value of continuous likelihood ratios over dichotomous cutoffs.