William DeGroat

This paper introduces MPRabc, an MPRA-informed model that improves enhancer-gene interaction prediction across cell types.

This paper introduces 3D IntelliGenes, a tool for visualizing AI/ML results on multi-omics data to discover biomarkers and predict diseases through 3D clustering and feature analysis.

VAREANT is a bioinformatics tool that simplifies variant data preprocessing, annotation, and AI/ML preparation for genomic analysis.

The paper develops a multimodal AI/ML framework to identify biomarkers and predict cardiovascular disease using multi-omics data, finding a 27-component signature with high predictive accuracy.

The paper constructs enhancer-promoter interaction networks during neural differentiation and identifies their temporal dynamics and disease-associated variant enrichment using computational and experimental methods.

This study identifies 18 significant transcriptomic biomarkers for cardiovascular disease with 96% accuracy using a combination of statistical tests and machine learning classifiers.

The paper integrates gene expression, genetic variants, and clinical data from high-risk Heart Failure patients to identify differentially expressed genes and functionally relevant mutations associated with CVD.