Katrina K. Hoyer

The paper uses single-cell transcriptomics to study the zebrafish sox17 lineage during gastrulation, revealing transcriptional changes in endoderm and dorsal forerunner cells.

The paper investigates the early immune response to Coccidioides in mice, identifying PD-L1+ neutrophils and Spp1+ macrophages as key players in inflammation and fibrosis.

This study uses single-cell RNA sequencing and spatial transcriptomics to analyze lung immune responses during lethal Coccidioides infection, identifying macrophages and neutrophils involved in fibrosis and immune dysregulation.

scANNA is an interpretable deep learning model for scRNA-seq that uses neural attention to learn gene associations, enabling downstream analyses without retraining.

N-ACT is an interpretable deep learning model for automatic cell type identification that detects salient genes, outperforming conventional methods in accuracy and scalability.

ACTIVA generates realistic synthetic scRNA-seq data with cell-type identification, improving classifier performance and reducing training time compared to existing methods.

The paper identifies Th17 and Treg cell balance as predictors of Coccidioides infection outcome in pediatric patients, finding higher Treg frequencies in persistent infections.