Hebing Chen

CellLoop identifies single-cell chromatin loops using a density-based framework, improving accuracy in mouse brain and embryogenesis data.

This paper benchmarks sequence-function deep learning models using ATAC-seq data, showing that different models and interpretability methods are better suited to specific tasks.

The paper maps long-range enhancer networks in human, macaque, and mouse fetal cortex, showing human-specific enhancers and neuropsychiatric disorder-linked SNPs regulate gene expression during neurogenesis.

The paper investigates structural variations and 3D chromatin organization in pancreatic cancer using SMRT and Hi-C sequencing, revealing their interplay and impact on gene expression and genome stability.

DeepHiC enhances Hi-C data resolution using a generative adversarial network, improving chromatin loop and TAD detection accuracy from low-coverage sequencing data.

The paper identifies and analyzes A-to-I RNA editing events in radiation-induced malignantly transformed cells, finding that SNPs affect ADAR2 activity and contribute to cancer progression.

The paper models the time-resolved succession of epigenetic regulation during early mammalian development and identifies key epigenetic events that coordinate transcriptional programs at different stages.

This study integrates multiple analyses to characterize human essential genes and develops a web platform for their comprehensive interpretation.

iFORM is a tool that improves the accuracy and sensitivity of TF motif scanning by integrating results from multiple motif discovery programs.