Xianfu Yi

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.

The paper identifies causal regulatory elements in complex genetic loci using CRISPR perturbations and shows that many causal variants lack classical epigenetic markers.

The paper investigates m1A RNA modification in neurons under normal and OGD/R conditions, identifying its regulatory roles in noncoding RNAs and ceRNA 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.