Author

P. Shuai

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Cancer Biology· 1 Pathology· 1 Plant Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Assay:scRNA-seq· 2 Approach:statistical model· 2 Research Focus:cell-cell communication· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Organism:human· 1

Frequent Co-Authors

L. Wang· 2 shared Y. Liu· 2 shared B. Gong· 1 shared C. Ku· 1 shared C. Li· 1 shared C.-C. Tung· 1 shared C.-E. Huang· 1 shared C.-L. Yang· 1 shared

Activity by Year

2019-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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2 shared papersCancer Biology

L. Wang

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Revised International Staging System (R-ISS) stage-dependent analysis uncovers oncogenes and potential immunotherapeutic targets in multiple myeloma

B. Gong, X. Yuan, Q. Zhang, T. Jiang, H. Li, J. Xiao, C. Li, L. Luo, P. Shuai, L. Wang, Y. Liu, M. Yu, Y. Shi, W. Zhang, Y. Zhang, L. Zhong

This study constructs a single-cell transcriptome atlas of multiple myeloma patients stratified by R-ISS, identifies PC subsets and immunotargets, and links them to prognosis and EBV infection.

bioRxiv · Dec 6, 2021 · PreprintCancer Biology

Single-cell transcriptomics unveils xylem cell development and evolution

C.-C. Tung, S.-C. Kuo, C.-L. Yang, C.-E. Huang, J.-H. Yu, Y.-H. Sun, P. Shuai, J.-C. Su, C. Ku, Y.-C. J. Lin

This study uses single-cell transcriptomics to analyze xylem cell development and evolution across four angiosperms, revealing conserved and variable lineage patterns.

bioRxiv · Oct 16, 2021Plant Biology

Big data reveals deep associations in physical examination indicators and can help predict overall underlying health status

H. Wang, P. Shuai, Y. Deng, J. Yang, S. Zhang, Y. Yin, L. Wang, D. Li, T. Yong, Y. Liu, L. Huang

The paper systematically analyzes correlations between 221 physical examination indicators in healthy and disease states, identifies disease markers, and develops machine learning models to predict health status with high precision.

bioRxiv · Nov 26, 2019 · PreprintPathology