Author

Tetsuya Sakurai

University of TsukubaORCID 0000-0002-5789-7547Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Signature Assays, Organisms & Methods

Click to filter · or open tag

Organism:human· 3 Approach:deep learning· 2 Assay:spatial transcriptomics· 2 Research Focus:cell-cell communication· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1 Approach:diffusion model· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

Xiucai YeiD· 3 shared Kenta NakaiiD· 2 shared Leyi WeiiD· 2 shared Ruheng WangiD· 2 shared Yang CuiiD· 2 shared Yaxuan Cui· 2 shared Zheyong Zhu· 2 shared Feifei CuiiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

3 shared papersBioinformatics

Xiucai YeORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Multimodal phenotyping reveals plant-food-related dietary information in serum Raman spectra of community-dwelling Japanese adults

Kenichi Hiraga, Takafumi Miyamoto, Teppei Nishino, Jieun Yoon, Kyohei Shibuya, Xiucai Ye, Tianyi Shi, Weike Liu, Hisato Suzuki, Hiroko Isoda, Tetsuya Sakurai, Hiroyuki Nishiyama, Tomohiro Okura, Motohiro Sekiya

The paper links dietary patterns with serum Raman spectra and metabolomics in Japanese adults, showing dietary information is distributed across complementary phenotypic layers.

bioRxiv · Oct 15, 2026 · PreprintBioinformatics

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