Establishing statistical inference theory for bio-systems and biological control theory using control engineering
Project/Area Number |
17K00398
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Research Field |
Life / Health / Medical informatics
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Research Institution | The University of Tokyo |
Principal Investigator |
Kiryu Hisanori 東京大学, 大学院新領域創成科学研究科, 准教授 (80415778)
|
Project Period (FY) |
2017-04-01 – 2021-03-31
|
Project Status |
Completed (Fiscal Year 2020)
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Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
|
Keywords | 生命情報学 / カルマンフィルター / 微分方程式 / 機械学習 / 1細胞シーケンシング / バイオテクノロジー / バイオインフォマティクス / 確率微分方程式 / 一細胞RNAシーケンシング / 一細胞シーケンシング / 制御工学 / RNA-seq / イネ / トランスクリプトーム |
Outline of Final Research Achievements |
Due to the low cost of next generation sequencing experiments and the high performance of microscopes, there has been an increase in research on measuring changes in the state of life over time at the cellular level. In general, time-series data are expected to provide more accurate estimates of causal relationships among elements than data measured only at a single time point. However, at present, descriptive analysis methods such as clustering are mainly used to analyze these data, and there is not much research on estimating the mechanisms that cause life state changes from measurement data. Therefore, we have developed and implemented a new set of algorithms to apply the theory of Kalman filter, which is widely used in the field of control engineering, to biological data.
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Academic Significance and Societal Importance of the Research Achievements |
生命情報科学の分野では人工知能や機械学習といった最新のデータ科学技術を用いたデータ解析が数多く行われているが、これらの技術が既存の確立した物理・化学・生物学の知識と無矛盾な結果を出す保証はなく、自然現象とは関係ないデータの特徴を捉えているのではないかという懸念が常に残る。そこで我々は、制御工学の分野で用いられているカルマンフィルターの理論を活用して、微分方程式のパラメータを測定データから推定する手法を開発した。この手法を用いれば、既知の生命過程の知識を人工知能や機械学習のモデルと統合することが容易になるため、理論生物学の強力な道具立てになることが期待される。
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Report
(5 results)
Research Products
(6 results)
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[Journal Article] LincRNA alleviates cardiac systolic dysfunction under pressure overload.2020
Author(s)
Kuwabara Y, Tsuji S, Nishiga M, Izuhara M, Ito S, Nagao K, Horie T, Watanabe S, Koyama S, Kiryu H, Nakashima Y, Baba O, Nakao T, Nishino T, Sowa N, Miyasaka Y, Hatani T, Ide Y, Nakazeki F, Kimura M, Yoshida Y, Inada T, Kimura T, Ono K.
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Journal Title
Commun Biol.
Volume: 3
Issue: 1
Pages: 434-434
DOI
Related Report
Peer Reviewed / Open Access / Int'l Joint Research
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[Journal Article] SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation2017
Author(s)
Matsumoto, H., Kiryu, H., Furusawa, C., Ko, S.H., M., Ko, B.H., S., Gouda, N., Hayashi, T., Nikaido, I.
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Journal Title
Bioinformatics
Volume: 印刷中
Issue: 15
Pages: 2314-2321
DOI
Related Report
Peer Reviewed / Open Access / Acknowledgement Compliant
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