2022 Fiscal Year Final Research Report
Geometric and functional data analysis for spatiotemporal data mining in medicine and life sciences
Project/Area Number |
21K21316
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Research Category |
Grant-in-Aid for Research Activity Start-up
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Allocation Type | Multi-year Fund |
Review Section |
1002:Human informatics, applied informatics and related fields
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Research Institution | Kyoto University |
Principal Investigator |
Okada Daigo 京都大学, 医学研究科, 助教 (10911852)
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Project Period (FY) |
2021-08-30 – 2023-03-31
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Keywords | 幾何学 / 関数データ解析 / 老化 / オミックス |
Outline of Final Research Achievements |
In this study, we developed a novel data analysis method based on geometry and functional data analysis for data-driven analysis in the medical and life sciences domain with temporal and spatial structures. In particular, we developed a novel DICNAP method based on functional data analysis to comprehensively detect nonlinear aging changes and classify their representative patterns in the aging genomics foeld. By applying this method to a public DNA methylation dataset, we were able to reveal the overall picture of nonlinear aging changes in DNA methylation markers.
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Free Research Field |
計算生物学
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Academic Significance and Societal Importance of the Research Achievements |
加齢に過程で生じる変化には、線形なものだけでなく非線形な変化も数多くあることが知られている。本研究の学術的意義は、オミックスデータからデータ駆動的に加齢変化パターンの全体像を取得する関数データ解析に基づく手法を開発したことである。また、関数データ解析を用いたオミックス解析の有意義な実施例を示したことも意義の1つである。
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