Development of Bayesian Estimation Method based on Information Geometry for Multi-layered Omics Data Integration
| Project/Area Number |
17K07254
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Medical genome science
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| Research Institution | Kyoto University |
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Principal Investigator |
Yamada Ryo 京都大学, 医学研究科, 教授 (50301106)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2017: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | オミックス / 解析手法 / ベイズ / ＭＣＭＣ / ゲノム / MCMC / オミクス / ベイズ推定 / 情報幾何 |
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| Outline of Final Research Achievements |
Initially we surveyed the appropriate targets of Bayesian integration of multiple omics layers and developed a method and did a poster presentation. Unfortunately a study in the similar frame was published by overseas competitors. Upon this, we re-directed our study targets of MCMC Bayesian approach to the phenotypes that are difficult to handle, 3-dimensional shape and 3-dimensional movement, and successfully developed a method to extract meaningful features from them so that those phenotypes can be readily integrated with single cell omics data set. The finding was proposed in a domestic meeting.
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| Academic Significance and Societal Importance of the Research Achievements |
海外他研究者による先行発表により、当初計画を変更して取り組むことを余儀なくされたが、オミックスデータの定義を拡張し、特に、統計解析の枠組みに乗りにくい表現型である、形態学的情報と３次元移動・軌跡情報とを標的として、ＭＣＭＣベイズ手法の開発に成功した。このようにして抽出した１細胞の形・動きの特徴量は、いわゆる１細胞情報（とさらに統合するのが容易な状態になっている。その基本的手法の枠組みを維持しつつ、標的に軌道修正を加えることにより、かえって、オミックス研究領域における解析の難しい表現型の解析基盤を整えることに寄与することとなり、有意義なものとなった。
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Report
(4 results)
Research Products
(6 results)
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[Journal Article] A Geometry-Based Multiple Testing Correction for Contingency Tables by Truncated Normal Distribution2020
Author(s)
Basak, T., Nagashima, K., Kajimoto, S., Kawaguchi, T., Tabara, Y., Matsuda, F., Yamada, R.
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Journal Title
Statisitcs in Biosciences
Volume: 12
Issue: 1
Pages: 63-77
DOI
Related Report
Peer Reviewed / Open Access
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