Author

Yangqiu Li

Jinan UniversityORCID 0000-0002-0974-4036Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Organism:human· 2 Approach:deep learning· 1 Approach:experimental assay· 1 Assay:proteomics· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

An HeiD· 1 shared Binbin ZhengiD· 1 shared Henry LamiD· 1 shared Jiani WangiD· 1 shared Keren ZhangiD· 1 shared Oscar Junhong LuoiD· 1 shared Ruijun TianiD· 1 shared Ruilian XuiD· 1 shared

Activity by Year

2025-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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1 shared paperCancer Biology

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2 papers

Recent·Most cited

Evaluation of the nano Liquid Chromatography coupled with Zeno TOF 7600 Mass Spectrometer for Cell Type-Resolved Spatial Proteomics

Keren Zhang, Xi Wang, Wan He, Jiani Wang, An He, Binbin Zheng, Yangqiu Li, Ruilian Xu, Weina Gao, Henry Lam, Ruijun Tian

The paper evaluates a nanoLC-Zeno TOF platform for spatial proteomics, identifying protein groups and differential expression in gastric cancer tissue.

bioRxiv · Jun 23, 2025 · PreprintCancer Biology

MIST: an interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis

Wenpu Lai, Yangqiu Li, Oscar Junhong Luo

MIST is a deep learning framework that integrates single-cell transcriptomic and TCR data to analyze T cell function and antigen specificity, revealing heterogeneity in lung cancer CD8+ T cells and their response to immunotherapy.

Science Advances · Apr 4, 2025Peer-reviewedBioinformatics