Author

Zheyong Zhu

Shandong University

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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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Organism:human· 2 Assay:spatial transcriptomics· 2 Research Focus:clustering· 1 Research Focus:differential expression· 1 Approach:diffusion model· 1 Research Focus:dimensionality reduction· 1 Research Focus:imputation· 1

Frequent Co-Authors

Kenta NakaiiD· 2 shared Leyi WeiiD· 2 shared Ruheng WangiD· 2 shared Tetsuya SakuraiiD· 2 shared Xiucai YeiD· 2 shared Yang CuiiD· 2 shared Yaxuan Cui· 2 shared Feifei CuiiD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBioinformatics

Kenta NakaiORCID

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2 papers

Recent·Most cited

DiffusionST: A deep generative diffusion model-based framework for enhancing spatial transcriptomics data quality and identifying spatial domains

Yaxuan Cui, Yang Cui, Ruheng Wang, Zheyong Zhu, Xin Zeng, Kenta Nakai, Feifei Cui, Zilong Zhang, Hua Wang Shi, Yan Chen, Xiucai Ye, Tetsuya Sakurai, Leyi Wei

DiffusionST improves spatial transcriptomics data quality and identifies spatial domains through a diffusion model and graph convolutional network.

bioRxiv · Jun 12, 2025 · PreprintBioinformatics

DenoiseST: A dual-channel unsupervised deep learning-based denoising method to identify spatial domains and functionally variable genes in spatial transcriptomics

Yaxuan Cui, Ruheng Wang, Xin Zeng, Yang Cui, Zheyong Zhu, Kenta Nakai, Xiucai Ye, Tetsuya Sakurai, Leyi Wei

DenoiseST is a deep learning method for spatial transcriptomics that improves data imputation, clustering, and identification of functionally variable genes by reducing dropout noise.

bioRxiv · Mar 4, 2024 · PreprintBioinformatics