Leah S. VandenBosch

The paper designs cell type-specific enhancers using deep learning models trained on chromatin accessibility data and validates their function across human and mouse cell types.

The paper develops ML models to predict the impact of non-coding variants on cell-class-specific regulatory elements using single nucleus ATAC-seq data, achieving over 90% accuracy.

The paper trains a machine learning model on human retinal epigenomic data to predict the functional impact of non-coding regulatory variants.

The paper identifies loss of neurogenic gene expression and motif accessibility in mature Muller glia that limits their regenerative capacity.