Lisha Zhu

scPrediXcan improves transcriptome-wide association studies by integrating deep learning for cell-type-specific gene expression prediction, identifying more causal genes and explaining more GWAS loci in type 2 diabetes and systemic lupus erythematosus.

The paper identifies non-coding DNA variations affecting P53 and cMYC-driven diseases by analyzing co-occupancy of these transcription factors and correlating DNA variations with gene expression changes.

This study generates single-cell RNA and ATAC sequencing data from postmenopausal human fallopian tubes and ovaries, revealing distinct cell types and regulatory landscapes with implications for gynecologic diseases.

This study demonstrates the feasibility of creating multi-dimensional genomic maps for juvenile idiopathic arthritis using limited patient samples and identifies epigenetic and genetic features associated with the disease.

The paper investigates chromatin landscapes and genetic risk loci in JIA by analyzing chromatin marks and transcription in neutrophils and CD4+ T cells, finding that risk loci show specific chromatin features and transcriptional activity.