Author

Rikifumi Ohta

The University of TokyoORCID 0000-0002-8909-1687Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Cancer Biology· 1 Developmental Biology· 1 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Shinichi MorishitaiD· 2 shared Akio KikuchiiD· 1 shared Akiyoshi KakitaiD· 1 shared Alessandro FillaiD· 1 shared Alexandra DürriD· 1 shared Alexis BriceiD· 1 shared Andrew SingletoniD· 1 shared Atsushi IwataiD· 1 shared

Activity by Year

2021-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersCancer Biology

Shinichi MorishitaORCID

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3 papers

Recent·Most cited

Dysregulation of the p16-CDK6 axis characterizes HPV-unrelated p16-positive oropharyngeal carcinoma

Kazuki Hayashi, Miki Kobayashi, Taku Kitano, Takahito Fukusumi, Toshihiro Kishikawa, Takashi Fujii, Rikifumi Ohta, Shinichi Morishita, Eiji Hara, Hidenori Inohara, Tomonori Matsumoto

This study identifies dysregulation of the p16-CDK6 axis in HPV-unrelated p16-positive oropharyngeal carcinomas, revealing a distinct molecular mechanism and clinical implications.

bioRxiv · Aug 19, 2026 · PreprintCancer Biology

Genome-wide association study identifies a new susceptibility locus in PLA2G4C for Multiple System Atrophy.

Yasuo Nakahara, Jun Mitsui, Hidetoshi Date, Kristine Joyce L. Porto, Yasuhiro Hayashi, Atsushi Yamashita, Yoshio Kusakabe, Takashi Matsukawa, Hiroyuki Ishiura, Tsutomu Yasuda, Atsushi Iwata, Jun Goto, Yaeko Ichikawa, Yoshio Momose, Yuji Takahashi, , Shoji Tsuji

This study identifies a new susceptibility locus, PLA2G4C, for Multiple System Atrophy through a genome-wide association study and functional analysis of a risk variant.

medRxiv · May 2, 2023 · PreprintGenetic & Genomic Medicine

A novel cell segmentation method for developing embryos using machine learning

Rikifumi Ohta, Takahiro Ide, Tatsuo Michiue

This paper introduces a machine learning method for cell segmentation in developing embryos, showing improved accuracy over existing techniques.

Development Growth & Differentiation · Aug 28, 2021Peer-reviewedDevelopmental Biology