Linear predictive coding and wavelet decomposition for robust microarray data clustering

Istepanian, R.S.H., Sungoor, A. and Nebel, J-C. (2007) Linear predictive coding and wavelet decomposition for robust microarray data clustering. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society ; 22 Aug - 26 Aug 2007 , Lyon, France. ISSN (print) 1557-170X ISBN 9781424407873

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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. Genomic signal processing is a new area of research that combines genomics with digital signal processing methodologies. In this paper, we present a comparative analysis of two genomic signal processing methods namely Linear Predictive Coding and Discrete Wavelet Decomposition for robust microarray data clustering. Vector quantization is applied to the resultant coefficients to provide the clustering of the data samples. Both techniques were validated for standard data sets. Comparative analyses of the results indicate that these methods provide improved clustering accuracy compared to some conventional clustering techniques. Moreover, there classifiers don't require any prior training procedures.

Item Type: Conference or Workshop Item (Paper)
Event Title: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Uncontrolled Keywords: gene-expression data, component analysis, tumor classification, cancer
Research Area: Computer science and informatics
Faculty, School or Research Centre: Faculty of Computing, Information Systems and Mathematics (until 2011)
Faculty of Computing, Information Systems and Mathematics (until 2011) > Digital Imaging Research Centre (DIRC)
Faculty of Computing, Information Systems and Mathematics (until 2011) > Mobile Information and Network Technology (MINT) Research Centre
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Depositing User: Automatic Import Agent
Date Deposited: 07 May 2010 13:19
Last Modified: 26 May 2010 10:03
URI: http://eprints.kingston.ac.uk/id/eprint/8449

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