Jun Wang

SpatialFormer integrates spatial and gene expression data to improve tasks like batch correction and cell-type annotation in spatial transcriptomics.

DeepLocRNA predicts RNA subcellular localization using transfer learning from pre-trained RBP-binding models, achieving state-of-the-art performance across human and mouse RNA.

The paper identifies age-related DNA hypermethylation at specific human tRNA gene loci using DNA methylome data from peripheral blood.

The paper introduces TRAP-seq to quantify CRISPR editing efficiency and uses it to design improved gRNAs for Cas9, ABE, and CBE with high accuracy and efficiency.

This study performs a meta-analysis of Scandinavian schizophrenia exomes, identifying three genes associated with the disorder and highlighting shared genetic architecture with high-functioning autism.

The paper identifies DNA motifs associated with methylation and unmethylation and shows their functional relevance in DNA methylation regulation and cancer.

The paper develops a CRISPR on-target activity prediction model called GNL using Bayesian Ridge Regression, showing improved cross-dataset and cross-species performance.