| Project/Area Number |
16300088
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Statistical science
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| Research Institution | The Institute of Statistical Mathematics |
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Principal Investigator |
EGUCHI Shinto The Institute of Statistical Mathematics, Dept. of Mathematical Analysis and Statistical Inference, Prof. (10168776)
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| Co-Investigator(Kenkyū-buntansha) |
FUJISAWA Hironori The Institute of Statistical Mathematics, Dept. of Mathematical Analysis and Statistical Inference, Assoc. Prof. (00301177)
HEMMI Masayuki The Institute of Statistical Mathematics, Dept. of Mathematical Analysis and Statistical Inference, Assist. Prof. (80465921)
MATSUURA Masaaki Japanese Foundation for Cancer Research, Genome Center, Group Leader (40173794)
TAKASHI Takenouchi Nara Institute of Science and Technology, Information Science, Researcher (50403340)
MASANORI Kawakita Kyushu University, Department of Computer Science and Communication Engineering, Assist. Prof. (90435496)
宮田 敏 癌研究会ゲノムセンター, 情報解析部門, 研究員 (60360343)
牛嶋 大 癌研究会ゲノムセンター, 情報解析部門, 研究員 (60328565)
村田 昇 早稲田大学, 理工学部電気電子情報工学科, 教授 (60242038)
栗木 哲 統計数理研究所, 数理・推論研究系, 教授 (90195545)
池田 思朗 統計数理研究所, 数理・推論研究系, 助教授 (30336101)
金森 敬文 東京工業大学, 大学院・情報理工学研究科, 助手 (60334546)
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| Project Period (FY) |
2004 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥14,290,000 (Direct Cost: ¥13,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2007: ¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2006: ¥3,100,000 (Direct Cost: ¥3,100,000)
Fiscal Year 2005: ¥3,300,000 (Direct Cost: ¥3,300,000)
Fiscal Year 2004: ¥3,600,000 (Direct Cost: ¥3,600,000)
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| Keywords | machine learning / statistical inference / Boosting / gene expression / protein expression / SNP / proteome / ゲノムデータ / マイクロアレイ / パタン認識 / メタアナライシス / 関数データ解析 / 独立成分分析 / 自己組織化主成分 / 多重比較 / 一塩基多型 / バイオインフォマティクス / バイアスモデル |
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| Research Abstract |
In this project we contributed on building up a new paradigm in statistical science. In particular, we focus on developing statistical methods for conducting rational reasoning induced from genome data. The study aims at discovery of genes strongly associated with a difficult disease by statistical method, and identification of SNPs that are related with drug effect and sensitivity. For this objective we propose and implement new methodology that works these specific targets as summarized in the following.
1. Group-Boost for the association study of gen expressions and phenotypes
2. Common peak approach for identifying peaks as a biomarker of phenotypes.
3. SNP identification for predicting drug effects and sensitivities.
4. A unified research in machine learning for genome data analyses
In these developments we are further devoting to extending conventional methods including boosting, independent/principal, component analysis, clustering to more flexible and understandable methods. This future works is expected to a wide and universal promotion in statistical science. Finally we acknowledge many researches in Jananese Foundation for Cancer Research for kind and instructive advices.
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