Wangjie Zheng

The paper develops cell-type-specific polygenic risk scores for CAD using scRNA-seq data and identifies lipid interactions specific to adipocytes and other cell types.

EA-PheWAS improves gene-phenotype discovery by integrating phenotype embeddings with PheWAS, outperforming conventional methods in detecting associations.

scELMo uses Large Language Models to analyze single-cell data for clustering, batch correction, and cell-type annotation without retraining, outperforming existing methods.

PERADIGM identifies rare disease-associated genes by integrating phenotype embeddings and patient similarity, outperforming traditional methods in detecting candidate genes for ADPKD, Marfan syndrome, and NF1.

This paper introduces EEPRS, a framework that improves polygenic risk prediction by integrating electronic health records through phenotype embeddings, showing significant performance gains over traditional methods.