Research field (1)：
Biological, health, and medical informatics
Research keywords (3)：
Machine learning
, Systems biology
, Information theory
Research theme for competitive and other funds (7)：
2020 - 2022 Development of transomics data analysis methods based on information theory
2018 - 2021 Deciphering positive and negative selection in thymocyte from information theoretic approach
2018 - 2020 データ駆動アプローチからのトランスオミクスネットワーク推定法の開発
2016 - 2018 多階層オミクスデータのためのネットワーク推定方法の開発
2015 - 2018 Elucidation of insulin signal transduction mechanism with mathematical analysis of network dynamics
2014 - 2016 疎性を用いた多階層ネットワークの同定
2013 - 2016 Information theoretical analysis of signaling network
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Papers (44)：
Yuki Ito, Shinsuke Uda, Toshiya Kokaji, Akiyoshi Hirayama, Tomoyoshi Soga, Yutaka Suzuki, Shinya Kuroda, Hiroyuki Kubota. Comparison of hepatic responses to glucose perturbation between healthy and obese mice based on the edge type of network structures. Scientific reports. 2023. 13. 1. 4758-4758
Suguru Fujita, Yasuaki Karasawa, Masashi Fujii, Ken-Ichi Hironaka, Shinsuke Uda, Hiroyuki Kubota, Hiroshi Inoue, Yohei Sumitomo, Akiyoshi Hirayama, Tomoyoshi Soga, et al. Four features of temporal patterns characterize similarity among individuals and molecules by glucose ingestion in humans. NPJ systems biology and applications. 2022. 8. 1. 6-6
Fumiko Matsuzaki, Shinsuke Uda, Yukiyo Yamauchi, Masaki Matsumoto, Tomoyoshi Soga, Kazumitsu Maehara, Yasuyuki Ohkawa, Keiichi I Nakayama, Shinya Kuroda, Hiroyuki Kubota. An extensive and dynamic trans-omic network illustrating prominent regulatory mechanisms in response to insulin in the liver. Cell reports. 2021. 36. 8. 109569-109569
Riku Egami, Toshiya Kokaji, Atsushi Hatano, Katsuyuki Yugi, Miki Eto, Keigo Morita, Satoshi Ohno, Masashi Fujii, Ken-ichi Hironaka, Saori Uematsu, et al. Trans-omic analysis reveals obesity-associated dysregulation of inter-organ metabolic cycles between the liver and skeletal muscle. iScience. 2021. 24. 3. 102217-102217
Wada T, Wataya M, Fujii M, Hironaka K, Eto M, Uda S, Hoshino D, Kunida K, Inoue H, Kubota H, et al. Single-cell information analysis reveals small intra- and large intercellular variations increase cellular information capacity. bioRxiv. 2019
