Michael A. Zeller

This study examines sialic acid receptor distribution and H5N1 virus binding in sheep, goats, and alpacas, finding species-specific differences in receptor abundance and virus binding.

This paper introduces EscaPRRS, a Bayesian variational autoencoder that predicts antibody escape propensities of PRRSV GP5 mutants using structural and sequence data, showing strong correlation with binding affinities.

This paper introduces a dynamic classification system for PRRSV-2 genetic variants using clustering algorithms and demonstrates its effectiveness in tracking genetic diversity over time.

The paper shows human seasonal H3N2 influenza A viruses can transmit to pigs but are not sustained in the pig population, with limited spread and transmission observed.

This study compares phylogenetic-based methods for fine-scale classification of PRRSV-2 ORF5 sequences, identifying robust approaches that improve resolution over RFLP-typing.

The paper introduces a logistic regression pipeline for classifying genetic sequences, achieving high accuracy even with degraded data and identifying clade-defining mutations.

This study uses machine learning to predict antigenic drift in swine influenza A viruses and validates predictions with experimental assays.