English  |  正體中文  |  简体中文  |  Items with full text/Total items : 49433/84396 (59%)
Visitors : 7470536      Online Users : 90
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/75818

    Title: Outlier Filtering for Identification of Gene Regulations in Microarray Time-Series Data
    Authors: Yang, Andy C.;Hsu, Hui-huang;Lu, Ming-da
    Contributors: 淡江大學資訊工程學系
    Keywords: Gene Expression Analysis;Gene Regulation Identification;Microarray;Outlier Filtering;Time-Series Data
    Date: 2009-03
    Issue Date: 2012-04-17 11:27:07 (UTC+8)
    Publisher: N.Y.: IEEE (Institute of Electrical and Electronic Engineers)
    Abstract: Microarray technology provides an opportunity for scientists to analyze thousands of gene expression profiles simultaneously. Time-series microarray data are gene expression values generated from microarray experiments within certain time intervals. Scientists can infer gene regulations in a biological system by judging whether two genes present similar gene expression values in microarray time-series data. Recently, a great many methods are widely applied on microarray time-series data to find out the similarity and the correlation degree among genes. Existing approaches including traditional Pearson coefficient correlation, Bayesian networks, clustering analysis, classification methods, and correlation analysis have individual disadvantages such as high computational complexity or they may be unsuitable for some microarray data. Traditional Pearson correlation coefficient is a numeric measuring method which gives novel effectiveness on two sets of numeric data. However, it is not suitable to be applied on microarray time-series data because of the existence of outliers among gene expression values. This paper presents a novel method of applying Pearson correlation coefficient along with an outlier filtering procedure on the widely-used microarray time-series datasets. Results show that the proposed method produces a better outcome compared with traditional Pearson correlation coefficient on the same dataset. Results show that the proposed method not only can find out certain more known regulatory gene pairs, but also keeps rational computational time.
    Relation: Proceedings of the Third International Conference on Complex, Intelligent and Software Intensive Systems (CISIS 2009), pp.854-859
    DOI: 10.1109/CISIS.2009.70
    Appears in Collections:[資訊工程學系暨研究所] 會議論文

    Files in This Item:

    File Description SizeFormat
    Outlier Filtering for Identification of Gene Regulations in Microarray Time-Series Data.pdf全文檔348KbAdobe PDF168View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback