Dandan Huang

The paper introduces hEVO, a genomic foundation model trained on human haplotype sequences, achieving top performance in variant interpretation and functional annotation across multiple benchmarks.

The paper uses single-cell multiome data to identify QTLs regulating gene expression and chromatin accessibility during cellular senescence, revealing new causal variants and regulatory mechanisms.

This study introduces a deep learning framework, FIND, to better classify genetic variants by their deleteriousness and trait-modulating effects, showing improved performance over existing methods.

This study identifies causal genes and transcriptomic factors influencing human telomere length using placental tissue data and GWAS.

The paper develops DeepAnchor, a deep learning model to characterize CTCF binding patterns and predict CTCF-anchored loops across human tissues, revealing sequence and chromatin features that influence loop formation and disease mechanisms.

This paper develops regBase, a database of whole genome base-wise aggregations of regulatory variant predictions, and three composite models to score functional, pathogenic, and cancer driver variants, showing superior performance in fine-mapping causal variants.