2010 Fiscal Year Final Research Report
Flexible and Robust Nonlinear Statistical Modeling Based on High-Dimensional Complex Heterogeneous Data Analysis
Project/Area Number |
20680016
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Research Category |
Grant-in-Aid for Young Scientists (A)
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Allocation Type | Single-year Grants |
Research Field |
Statistical science
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Research Institution | The University of Tokyo |
Principal Investigator |
IMOTO Seiya The University of Tokyo, 医科学研究所, 准教授 (10345027)
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Project Period (FY) |
2008 – 2010
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Keywords | 高次元データ解析 / 異種データ統合 / ベイズ統計 / ロバスト |
Research Abstract |
We developed robust statistical methods for extracting valuable information from various types of high-dimensional heterogeneous data. As the results, when a subset of feature variables is defined a priori, we develop a series of statistical methods that can evaluate whether the subset has a unique distribution by comparing with background variables in the given large dataset. We also proposed a robust estimation method for large gene network from microarray gene expression data and other types of data like transcription binding sites and applied it to the analysis of cancer heterogeneity.
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[Journal Article] Unraveling dynamic activities of autoacine pathways that control drug-response transcriptome networks2009
Author(s)
Y.Tamada, H.raki, S.Imoto, M.Nagasaki, A.Doi, Y.Nakinishi, Y.Tomiyasu, K.Yasuda, B.Dunmore, D.Sanders, S.Humphries, C.Print, D.S.Charnock-Jones, K.Tashiro, S.Kuhara, S.Miyano
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Journal Title
Pacific Symposium on Biocomputing 14
Pages: 251-263
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[Remarks] ホームページ等