Zhanlin Chen

Validation of Enhancer Regions in Primary Human Neural Progenitor Cells using Capture STARR-seq

The paper identifies and validates enhancer regions in primary human neural progenitor cells using capture STARR-seq, linking them to genes involved in psychiatric disease pathways.

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

The paper introduces the EN-TEx resource, which provides personal epigenomes and variant-impact models across multiple tissues, revealing allele-specific loci and their associations with GWAS loci.

DeepVelo: Data-driven Single-cell Transcriptomic Deep Velocity Field Learning with Neural Ordinary Differential Equations

DeepVelo models single-cell transcriptome dynamics using neural ODEs, improving velocity field accuracy and identifying developmental driver genes.

DECODE: A Deep-learning Framework for Condensing Enhancers and Refining Boundaries with Large-scale Functional Assays

DECODE improves enhancer prediction and boundary detection using deep learning and functional assays, achieving higher accuracy and reduced annotation size.

SCAN-ATAC Sim: a scalable and efficient method for simulating single-cell ATAC-seq from bulk-tissue experiments

SCAN-ATAC-Sim simulates scATAC-seq data from bulk ATAC-seq to benchmark analysis methods with known cell-type labels.

DiNeR: a Differential Graphical Model for analysis of co-regulation Network Rewiring

DiNeR identifies significant changes in transcription factor co-regulation networks by analyzing ChIP-seq data from disease and normal samples, revealing key disease-associated regulators.