Gen Tamiya
Gen Tamiya (Ph.D.)
Title
Team Leader

Members

  • Team leader
    Gen Tamiya
  • Deputy team leader
    Motomichi Matsuzaki
  • Research scientist
    Rieko Sakurai
  • Research scientist
    Hikaru Matsuoka
  • Postdoctoral researcher
    Ayekoko Minn
  • Technical staff I
    Makoto Watanabe
  • Technical staff I
    Chiaki Murata
  • Technical staff II
    Sayako Osakabe
  • Technical staff II
    Yurii Kotsar
  • Visiting scientist
    Kazuharu Misawa
  • Visiting scientist
    Jun Takayama
  • Visiting scientist
    Masao Ueki
  • Visiting scientist
    Takayuki Takahashi
  • Visiting scientist
    Nagahisa Suzuki
  • Visiting scientist
    Takamitsu Funayama
  • Visiting scientist
    Junya Iwazaki
  • Visiting scientist
    Daisuke Saisho
  • Visiting scientist
    Matsuyuki Shirota
  • Student trainee
    Yuki Kashiwada
  • Student trainee
    Ryoma Hayashi

Introduction

Laboratory's photo

Using AI-based methods in statistical genetics, we analyze Big Data consisting of a wide variety of information such as genomic variations, lifestyle and health status together with an enormous medical literature in order to identify causal factors for complex common diseases. We aim to enable genomic risk prediction for complex common diseases and then to realize personalized and preventive medicine. Chiefly we investigate Big Data from large-scale prospective genomic cohorts such as the Tohoku Medical Megabank organization.

Main Research Field
Molecular Biology & Genetics
Research Field
Clinical Medicine / Agricultural Sciences / Computer Science / Mathematics
Research Subjects
Statistical genetics of prospective genomic cohort
Laboratory Website URL
http://www.genetix-h.com/~AMERI/
RIKEN Website URL
http://www.riken.jp/en/research/labs/aip/goalorient_tech/stat_genet/

Introduction Video

Statistical Genetics Team (PI: Gen Tamiya) thumbnails
Show details
Poster(s)

Research Achievements

List of Achievements

Related posts

posted on June 24, 2024 14:54Information
【Event Report】The 6th Annual Meeting of Japanese Associatin for Medical Artificial Intelligence (JMAI) (June 21-22, 2024, Nagoya)
posted on June 12, 2024 14:29InformationPressrelease
Publish: Body mass index stratification optimizes polygenic prediction of type 2 diabetes in cross-biobank analyses (Nature Genetics, June 12, 2024)
posted on May 8, 2024 14:18InformationPressrelease
Publish: Functional variants in a TTTG microsatellite on 15q26.1 cause familial non-autoimmune thyroid abnormalities (Nature Genetics, May 7, 2024)
posted on December 20, 2023 09:43Information
【Published】“Is Scientific Discovery Possible through AI and Simulation?”（Experimental Medicine, January 2024 Issue, YODOSHA、Decemer 20, 2023）
posted on June 23, 2023 19:16Information
【Event Report】The 5th Annual Meeting of Japanese Associatin for Medical Artificial Intelligence (JMAI) (June 17-18, 2023, Tokyo)
posted on May 15, 2023 21:09
The 5th Annual Meeting of Japanese Associatin for Medical Artificial Intelligence (JMAI)
posted on July 26, 2022 13:27Information
The 4th Annual Meeting of Japanese Associatin for Medical Artificial Intelligence (JMAI)
posted on May 13, 2022 12:28
The 4th Annual Meeting of Japanese Associatin for Medical Artificial Intelligence (JMAI)
posted on December 10, 2021 14:55Information
The power of genetic diversity in genome-wide association studies of lipids
posted on October 8, 2021 18:14Information
A cross-population atlas of genetic associations for 220 human phenotypes
posted on May 19, 2021 12:16Seminar
[29th AIP Open Seminar] Talks by Statistical Genetics Team
posted on January 13, 2021 15:58Information
Construction of the 1st version of "Japanese Reference Genome" (JG1) based on three Japanese
posted on February 4, 2020 10:17InformationAward
The 2nd Annual Meeting of the Japanese Association for Medical Artificial Intelligence (JMAI) was held
posted on January 23, 2020 13:25Award
Visiting Scientist Jun Takayama received the Japan Medical Research and Development Grand Prize
posted on November 27, 2019 14:50Information
Illuminating Clues of Cancer Buried in Prostate MR Image: Deep Learning and Expert Approaches
posted on July 13, 2018 09:30Pressrelease
Rice plants evolve to adapt to flooding