Yang Li

Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data

STREAM infers enhancer-driven gene regulatory networks from single-cell RNA-seq and ATAC-seq data, showing improved performance in TF recovery and enhancer-gene discovery.

A Weighted Two-stage Sequence Alignment Framework to Identify DNA Motifs from ChIP-exo Data

TESA improves TFBS identification in ChIP-exo data by integrating nucleotide-resolution signal with a two-stage alignment framework, showing higher accuracy in E. coli datasets.

CEMIG: Prediction of the cis-regulatory motif using the De Bruijn graph from ATAC-seq

CEMIG predicts cis-regulatory motifs from ATAC-seq data, outperforming existing methods and identifying cell-type-specific and shared TF motifs in GM12878 and K562 cells.

Spatially resolved transcriptomics reveals unique gene signatures associated with human temporal cortical architecture and Alzheimer's pathology

This study uses spatial transcriptomics to identify gene signatures in the human temporal cortex linked to Alzheimer's pathology.

DeepMAPS: Single-cell biological network inference using heterogeneous graph transformer

DeepMAPS infers biological networks from single-cell multi-omics data using a heterogeneous graph transformer, outperforming existing tools in cell clustering and pathway prediction.

Systematic Prediction of Regulatory Motifs from Human ChIP-Sequencing Data Based on a Deep Learning Framework

The paper introduces DESSO, a deep learning framework for predicting regulatory motifs from ChIP-Seq data, showing improved accuracy over existing tools.

Ultra-fast and accurate motif finding in large ChIP-seq datasets reveals transcription factor binding patterns

ProSampler efficiently identifies transcription factor binding motifs in large ChIP-seq datasets, revealing complex motif patterns and cooperative binding mechanisms.