Chloe Mattila

RKMR identifies predictive marker panels in spatial omics data using a scalable framework that integrates kernel modeling and spatial dependence.

FusedFCR identifies marker genes associated with developmental transitions in time-course scRNA-seq data by combining lasso and fusion penalties for interpretable gene selection.

CBKMR improves marker detection in omics data by capturing nonlinear gene-outcome relationships and interactions, outperforming existing methods in accuracy and uncertainty quantification.

The paper introduces a Bayesian negative binomial regression model with group-structured priors for spatially correlated count data, applied to cancer registry and spatial omics datasets.