Author

Xuan Liang

Chinese Academy of SciencesORCID 0009-0008-6717-5039Unclaimed ORCID profile · follow to get notified of new preprints

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Xuan Liang1 paper · Peking University

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Assay:ATAC-seq· 1 Assay:bulk RNA-seq· 1 Approach:deep learning· 1 Assay:Hi-C· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Dongmei HaniD· 2 shared Wenwen ZhangiD· 2 shared Yuanyuan MiaoiD· 2 shared Yurun Li· 2 shared Zhen WangiD· 1 shared Zhen WangiD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

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6 top collaborators · 14 mutual co-author links

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2 shared papersBioinformatics

Dongmei HanORCID

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2 papers

Recent·Most cited

BioinformaticsAdvanced SciencePeer-reviewedOct 28, 2026

XChrom: a cross-cell chromatin accessibility prediction model integrating genomic sequence and cellular context

Yuanyuan Miao, Xuan Liang, Wenwen Zhang…·Zhen Wang

XChrom predicts chromatin accessibility across cells and genomic regions using genomic sequence and cell identity, outperforming existing methods and enabling cross-species analysis.

1 citation

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BioinformaticsNucleic Acids ResearchPeer-reviewedApr 23, 2026

Predicting enhancer-gene links from single-cell multi-omics data by integrating prior Hi-C information

Xuan Liang, Yuanyuan Miao, Dongmei Han…·Zhen Wang

This paper introduces SCEG-HiC, a machine learning method that predicts enhancer-gene links using single-cell multi-omics data and Hi-C prior knowledge, showing improved performance over existing models.

DOI 10.1093/nar/gkag437

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