Author

Hongli Lin

Xiamen UniversityPrimary ORCID profile (0000-0003-3403-7584 · 1 paper)Unclaimed ORCID profile · follow to get notified of new preprints

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Same name, identified by ORCID

Hongli LiniD1 paper · University of Hong Kong

Biology Disciplines

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Bioinformatics· 1 Genetics· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Research Focus:gene regulatory network inference· 2 Organism:human· 2 Organism:mouse· 2 Research Focus:benchmarking· 1 Research Focus:clustering· 1 Assay:CRISPR screen· 1 Assay:CUT&Tag· 1 Approach:deep learning· 1

Frequent Co-Authors

Jialiang HuangiD· 3 shared Fei Chen· 2 shared Jinxian Dai· 2 shared Yiding Yang· 2 shared Chaoyong Yang· 1 shared Danni Hong· 1 shared Falong LuiD· 1 shared Fei TengiD· 1 shared

Activity by Year

2022-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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3 shared papersGenomics

Jialiang HuangORCID

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3 papers

Recent·Most cited

eScreen: a deep learning framework for functionally decoding the regulatory genome at single-nucleotide resolution

Shijie Luo, Liquan Lin, Han Zhang, Yiding Yang, Yunxiang Liu, Yingli Han, Fei Chen, Ruipu Liang, Ximing Niang, Jinxian Dai, Run Chen, Hongli Lin, Zhijie Li, Yongying Peng, Fei Teng, Jialiang Huang

The paper introduces eScreen, a deep learning model that decodes regulatory genome function at single-nucleotide resolution using CRISPR screen data, achieving high accuracy and mechanistic interpretation.

bioRxiv · Feb 5, 2026 · PreprintGenomics

Super-CUT&Tag: A Sensitive, Spatially Resolved Approach for Epigenomic Profiling in Tissue Sections

Ming Zhu, Zhicheng Zha, Jian Xuan, Hanyu Zhao, Jinxian Dai, Jiamao Liu, Youyuan Xia, Hongli Lin, Yiding Yang, Fei Chen, Shichao Lin, Xuzhu Liu, Lingling Wu, Zhi Zhu, Jialiang Huang, , Chaoyong Yang

Super-CUT&Tag improves spatial epigenomic profiling by increasing sensitivity and resolution, revealing active-chromatin heterogeneity in mouse embryonic tissues.

bioRxiv · Dec 10, 2025 · PreprintGenetics

Complexity of enhancer networks predicts cell identity and disease genes revealed by single-cell multi-omics analysis

Danni Hong, Hongli Lin, Lifang Liu, Muya Shu, Jianwu Dai, Falong Lu, Mengsha Tong, Jialiang Huang

The paper introduces eNet, a method to build enhancer networks from single-cell data, showing complex networks regulate cell identity and disease genes better than existing models.

Briefings in Bioinformatics · Oct 25, 2022Peer-reviewedBioinformatics