Rchr
J-GLOBAL ID：201801009240642668
Update date: Jan. 30, 2024
Onimaru Koh
オニマル コウ | Onimaru Koh
Affiliation and department：
Job title：
BioEngineer (Data scientist)
Homepage URL (1)：
https://sites.google.com/site/kohonimaru/home
Research field (1)：
Evolutionary biology
Research keywords (5)：
Comparative genomics
, Machine learning
, Systems biology
, Evolutionary biology
, Biology
Research theme for competitive and other funds (5)：
- 2022 - 2025 深層学習を用いた非コードゲノム配列のがんドライバー変異の探索
- 2021 - 2022 深層学習を用いた非コード領域のがんドライバー変異の探索
- 2018 - 2021 ディープラーニングを用いた形態形成における遺伝子制御ネットワークの推測方法の確立
- 2017 - 2020 Development of transcriptional regulatory network prediction methods in morphogenesis
- 2017 - 2019 KAKENHI Grant-in-Aid for Young Scientists (B) 17K15132
Papers (20)：
-
Hiroshi I Suzuki, Koh Onimaru. Biomolecular condensates in cancer biology. Cancer science. 2022. 113. 2. 382-391
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Jonas Malkmus, Laurène Ramos Martins, Shalu Jhanwar, Bonnie Kircher, Victorio Palacio, Rushikesh Sheth, Francisca Leal, Amandine Duchesne, Javier Lopez-Rios, Kevin A. Peterson, et al. Spatial regulation by multiple Gremlin1 enhancers provides digit development with cis-regulatory robustness and evolutionary plasticity. Nature Communications. 2021. 12. 1
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Koh Onimaru, Luciano Marcon. Systems Biology Approach to the Origin of the Tetrapod Limb. Evolutionary Systems Biology. 2021. 89-113
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Koh Onimaru, Kaori Tatsumi, Chiharu Tanegashima, Mitsutaka Kadota, Osamu Nishimura, Shigehiro Kuraku. Developmental hourglass and heterochronic shifts in fin and limb development. eLife. 2021. 10
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Koh Onimaru, Osamu Nishimura, Shigehiro Kuraku. Predicting gene regulatory regions with a convolutional neural network for processing double-strand genome sequence information. PLOS ONE. 2020. 15. 7. e0235748-e0235748
ｍore...
Professional career (1)：
- PhD (Tokyo Institute of Technology)
Work history (6)：
Awards (2)：
- 2021/07 - Paper Award The evolutionary origin of developmental enhancers in vertebrates: Insights from non-model species
- 2019/09 - Poster award Predicting gene regulatory regions with a convolutional neural network for processing double-strand genome sequence information
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