Author

Matsuyuki Shirota

Tohoku UniversityORCID 0000-0002-7776-9964Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genetic & Genomic Medicine· 1 Genomics· 1 Plant Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 2 Organism:human· 2 Approach:statistical model· 2 Organism:Arabidopsis· 1 Research Focus:cell type annotation· 1 Research Focus:database / resource· 1 Research Focus:gene regulatory network inference· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Akihisa ShinozawaiD· 1 shared Akihito OtsukiiD· 1 shared Atsuo KikuchiiD· 1 shared Atsushi HozawaiD· 1 shared Atsushi ShimizuiD· 1 shared Chika Takano-Asai· 1 shared Chikahiko NumakuraiD· 1 shared Chinatsu Gocho· 1 shared

Activity by Year

2019-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 16 mutual co-author links

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1 shared paperPlant Biology

Akihisa ShinozawaORCID

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3 papers

Recent·Most cited

Dried blood spot proteomics as a diagnostic framework for citrin deficiency

Eriko Totsune, Daisuke Nakajima, Ryo Konno, Yasuko Mikami-Saito, Natsuko Arai‐Ichinoi, Hikaru Nishida, Hiroko Yagi, Takashi Ishige, Hoshiro Suzuki, Matsuyuki Shirota, Jun Takayama, Chika Takano-Asai, Masaru Shimura, Hideo Sasai, Tomoko Lee, , Yoichi Wada

This paper develops a dried blood spot proteomics method to diagnose citrin deficiency by quantifying SLC25A13 and identifying a 15-protein signature with high accuracy.

medRxiv · May 28, 2026 · PreprintGenetic & Genomic Medicine

Self-incompatibility phenotypes of SRK mutants can be predicted with high accuracy

Masaya Yamamoto, Shotaro Ohtake, Akihisa Shinozawa, Matsuyuki Shirota, Yuki Mitsui, Hiroyasu Kitashiba

The study identifies mutations in SRK that cause self-incompatibility defects and shows machine learning models can predict these phenotypes accurately.

New Phytologist · Sep 15, 2024Peer-reviewedPlant Biology

3.5KJPNv2, An allele frequency panel of 3,552 Japanese Individuals

Shu Tadaka, Fumiki Katsuoka, Masao Ueki, Kaname Kojima, Satoshi Makino, Sakae Saito, Akihito Otsuki, Chinatsu Gocho, Mika Sakurai‐Yageta, Inaho Danjoh, Ikuko N. Motoike, Yumi Yamaguchi‐Kabata, Matsuyuki Shirota, S. Koshiba, Masao Nagasaki, , Kengo Kinoshita

The paper constructs a population-level allele frequency panel of 3,552 Japanese individuals using a standardized pipeline and makes it publicly available.

bioRxiv · Jan 25, 2019 · PreprintGenomics