Author

Xiuhui Yang

Shandong UniversityORCID 0009-0002-8521-3533Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Pharmacology & Toxicology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Assay:scRNA-seq· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

Jun DingiD· 3 shared Koren Kathleen ManniD· 2 shared France DierickiD· 1 shared Hao WuiD· 1 shared Hao WuiD· 1 shared Hao WuiD· 1 shared Kiran MakhaniiD· 1 shared Natascha GagnoniD· 1 shared

Activity by Year

2024-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 26 mutual co-author links

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3 shared papersBioinformatics

Jun DingORCID

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3 papers

Recent·Most cited

Pharmacology & ToxicologyEnvironmental Health PerspectivesPeer-reviewedJan 1, 2025

Unveiling the Impact of Arsenic Toxicity on Immune Cells in Atherosclerotic Plaques: Insights from Single-Cell Multi-Omics Profiling.

Kiran Makhani, Xiuhui Yang, France Dierick…·Koren Kathleen Mann

This study uses single-cell multi-omics profiling to show arsenic alters macrophage transcription and epigenetics in atherosclerotic plaques, with more profound epigenetic changes.

7 citations

mousescRNA-seq

𝕏 PostBluesky

BioinformaticsCommunications BiologyPeer-reviewedJul 31, 2024

scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information

Yingfu Wu, Zhenqi Shi, Xiangfei Zhou…·Hao Wu

scHiCyclePred predicts cell cycle phases from single-cell Hi-C data using multi-scale interaction features, outperforming existing methods.

27 citationstop 5%

Hi-Cdeep learning

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BioinformaticsGenome biologyPeer-reviewedJul 29, 2024

scCross: A Deep Generative Model for Unifying Single-cell Multi-omics with Seamless Integration, Cross-modal Generation, and In-silico Exploration

Xiuhui Yang, Koren Kathleen Mann, Hao Wu, Jun Ding

scCross integrates and generates single-cell multi-omics data using deep generative models, enabling seamless cross-modal analysis and in-silico experiments.

85 citationstop 1%

scRNA-seqdeep learning

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