Author

T. Fujioka

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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Cell Biology· 2 Plant Biology· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Research Focus:cell type annotation· 2 Approach:experimental assay· 2 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Assay:imaging· 1

Frequent Co-Authors

A. Abe· 1 shared D. Song· 1 shared E. Kanzaki· 1 shared G. Takahashi· 1 shared H. Shimono· 1 shared H. Takagi· 1 shared H. Utsushi· 1 shared J. Li· 1 shared

Activity by Year

2018-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperPlant Biology

A. Abe

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Impact of rice GENERAL REGULATORY FACTOR14h (GF14h) on low-temperature seed germination and its application to breeding

Y. Sugimura, K. Oikawa, Y. Sugihara, H. Utsushi, E. Kanzaki, K. Ito, Y. Ogasawara, T. Fujioka, H. Takagi, M. Shimizu, H. Shimono, R. Terauchi, A. Abe

This paper identifies GF14h as the causal gene for low-temperature seed germination in rice and demonstrates its utility in breeding for direct seeding in cold climates.

bioRxiv · Feb 16, 2024 · PreprintPlant Biology

Retinoids rescue ceruloplasmin secretion and alleviate oxidative stress in Wilson's disease-specific hepatocytes

D. Song, G. Takahashi, Y.-W. Zheng, M. Matsuo-Takasaki, J. Li, M. Takami, Y. An, Y. Hemmi, N. Miharada, T. Fujioka, M. Noguchi, T. Nakajima, m. K. Saito, Y. Nakamura, T. Oda, , Y. Hayashi

This study uses patient-derived iPSCs to identify retinoids as potential therapeutics for Wilson's disease by rescuing ceruloplasmin secretion and reducing oxidative stress.

Human Molecular Genetics · Oct 28, 2022Peer-reviewedCell Biology

Automatic Single-Cell Discrimination by Cellular Appearance using Convolutional Neural Network

Y. Matsuoka, R. Nakatsuka, T. Fujioka

This paper shows that a convolutional neural network can classify hematopoietic tumor cell lines with >90% accuracy using bright-field images.

bioRxiv · Oct 30, 2018Cell Biology