2007 Fiscal Year Final Research Report Summary
New statistical methodology for genome diversity analysis
| 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)
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| Project Period (FY) |
2004 – 2007
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| Keywords | machine learning / statistical inference / Boosting / gene expression / protein expression / SNP / proteome |
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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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Research Products
(85 results)
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[Journal Article] Common peak approach using mass spectrometry data sets for predicting the effects of anticancer drugs on breast cancer.2007
Author(s)
M. Ushijima, S. Miyata, S. Eguchi, M. Kawakita, M. Yoshimoto, T. Iwase, F. Akiyama, G. Sakamoto, K. Nagasaki, Y. Miki, T. Noda, Y. Hoshikawa, M. Matsuura.
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Journal Title
Cancer Informatics 3
Pages: 285-293
Description
「研究成果報告書概要(欧文)」より
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[Journal Article] Genotypes at chromosome 22q12-13 are associated with HIV-1-exposed but uninfected status in Italians2005
Author(s)
Kanari, Y., Clerici, M., Abe, H., Kawabata, H., Trabattoni, D., Caputo, S.L., Mazzotta, F., Fujisawa, H., Niwa, A., lshihara, C., Takei, Y.A., and Miyazawa, M.
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Journal Title
Description
「研究成果報告書概要(和文)」より
Peer Reviewed
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