• DocumentCode
    1900031
  • Title

    Linear Predictive Coding for Enhanced Microarray Data Clustering

  • Author

    Istepanian, Robert S H ; Sungoor, Ala ; Nebel, Jean-Christophe

  • Author_Institution
    Kingston Univ. Kingston-Upon-Thames, Kingston upon Thames
  • fYear
    2007
  • fDate
    10-12 June 2007
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Microarrays are powerful tools for simultaneous monitoring of the expression levels of large number of genes. Their analysis is usually achieved by using clustering techniques. In this paper, we present a new clustering method based on Linear Predictive Coding to provide enhanced microarray data analysis. In this approach, spectral analysis of microarray data is performed to classify samples according to their distortion values. The technique was validated for a standard data set. Comparative analysis of the results indicates that this method provides improved clustering accuracy compared to some conventional clustering techniques. Moreover, our classifier does not require any prior training procedure.
  • Keywords
    diseases; genetic engineering; genetics; linear predictive coding; pattern clustering; array data clustering enhancement; linear predictive coding; microarray data analysis; parative analysis; Bioinformatics; Cardiovascular diseases; Data analysis; Data mining; Distortion measurement; Gene expression; Genomics; Linear predictive coding; Signal processing; Spectral analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Genomic Signal Processing and Statistics, 2007. GENSIPS 2007. IEEE International Workshop on
  • Conference_Location
    Tuusula
  • Print_ISBN
    978-1-4244-0998-3
  • Electronic_ISBN
    978-1-4244-0999-0
  • Type

    conf

  • DOI
    10.1109/GENSIPS.2007.4365815
  • Filename
    4365815