Author

Ye Li

Heilongjiang UniversityORCID 0000-0002-8353-9567Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Ye Li7 papers · Chinese Academy of Sciences

Biology Disciplines

Click to filter author papers

Bioinformatics· 2 Neuroscience· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:deep learning· 2 Research Focus:differential expression· 2 Assay:ATAC-seq· 1 Assay:ChIP-seq· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1 Approach:graph neural network· 1 Organism:human· 1

Frequent Co-Authors

Chunquan LiiD· 2 shared Bing LiuiD· 1 shared Chao SongiD· 1 shared Fan WangiD· 1 shared Fanhui Kong· 1 shared Guorui ZhangiD· 1 shared Hongmin CaiiD· 1 shared Hui CuiiD· 1 shared

Activity by Year

2022-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 15 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioinformatics

Chunquan LiORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

NeurosciencebioRxivPreprintMar 7, 2026

Modeling Multi-Modal Brain Connectomes for Brain Disorder Diagnosis via Graph Diffusion Optimal Transport Network

Xiaoqi Sheng, Jiawen Liu, Jiaming Liang…·Hongmin Cai

This paper introduces GDOT-Net, a graph diffusion optimal transport network that aligns structural and functional brain connectivity for improved diagnosis of neuropsychiatric disorders.

DOI 10.64898/2026.03.05.709693

humanspatial transcriptomics

𝕏 PostBluesky

BioinformaticsbioRxivPreprintMay 17, 2024

TRAPT: A multi-stage fused deep learning framework for transcriptional regulators prediction via integrating large-scale epigenomic data

Guorui Zhang, Chao Song, Mingxue Yin…·Chunquan Li

TRAPT predicts transcriptional regulators by integrating multi-omics epigenomic data, outperforming existing methods in identifying co-factors and chromatin regulators.

DOI 10.1101/2024.05.17.594242

ATAC-seqChIP-seq

𝕏 PostBluesky

BioinformaticsIEEE Transactions on Computational Biology and BioinformaticsPeer-reviewedJan 11, 2022

SENIES: DNA Shape Enhanced Two-layer Deep Learning Predictor for the Identification of Enhancers and Their Strength

Ye Li, Fanhui Kong, Hui Cui…·Jiquan Ma

SENIES is a deep learning predictor that identifies enhancers and their strength by integrating DNA shape features with sequence-derived features.

18 citations

DNA methylationwhole-genome sequencing

𝕏 PostBluesky