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    National Tsing Hua University Institutional Repository > 工學院  > 工業工程與工程管理學系 > 期刊論文 >  Cluster analysis of genome-wide expression data for feature extraction

    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/36593

    Title: Cluster analysis of genome-wide expression data for feature extraction
    Authors: Kuo-Sheng Lin;Chen-Fu Chien
    教師: 簡禎富
    Date: 2009
    Publisher: Elsevier
    Relation: Expert Systems with Applications, Volume 36, Issue 2, Part 2, March 2009, Pages 3327-3335
    Keywords: Bio-chip
    Gene expression
    Cluster analysis
    Data mining
    Feature extraction
    Abstract: © 2009 Elsevier - Bio-chip data that consists of high-dimensional attributes have more attributes than specimens. Thus, it is difficult to obtain covariance matrix from tens thousands of genes within a number of samples. Feature selection and extraction is critical to remove noisy features and reduce the dimensionality in microarray analysis. This study aims to fill the gap by developing a data mining framework with a proposed algorithm for cluster analysis of gene expression data, in which coefficient correlation is employed to arrange genes. Indeed, cluster analysis of microarray data can find coherent patterns of gene expression. The output is displayed as table list for convenient survey. We adopt the breast cancer microarray dataset to demonstrate practical viability of this approach.
    URI: http://www.elsevier.com/
    Appears in Collections:[工業工程與工程管理學系] 期刊論文

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