Di Huang

This paper identifies silencer variants as key drivers of gene upregulation in Alzheimer's disease using a deep learning framework combining bulk and single-cell epigenomic data.

The paper identifies super-silencers in B cells, showing their role in development and their conversion to super-enhancers in lymphoma, with implications for carcinogenesis.

The paper uses deep learning to analyze GWAS variants in silencer sequences across multiple tissues, finding strong disease associations and identifying specific SNPs linked to Parkinson's, schizophrenia, and Type 1 diabetes.