Istepanian, R. S. H., Sungoor, A. and Nebel, J.C. (2011) Comparative analysis of genomic signal processing for microarray data clustering. IEEE Transactions on Nanobioscience, 10(4), pp. 225-238. ISSN (print) 1536-1241Full text not available from this archive.
Genomic signal processing is a new area of research that combines advanced digital signal processing methodologies for enhanced genetic data analysis. It has many promising applications in bioinformatics and next generation of healthcare systems, in particular, in the field of microarray data clustering. In this paper we present a comparative performance analysis of enhanced digital spectral analysis methods for robust clustering of gene expression across multiple microarray data samples. Three digital signal processing methods: linear predictive coding, wavelet decomposition, and fractal dimension are studied to provide a comparative evaluation of the clustering performance of these methods on several microarray datasets. The results of this study show that the fractal approach provides the best clustering accuracy compared to other digital signal processing and well known statistical methods.
|Uncontrolled Keywords:||discrete wavelet, fractal dimension, genomic signal processing, linear predictive coding, microarray clustering, vector quantization, gene-expression data, partial least-squares, time-series data, component analysis, tumor classification, cancer, bioinformatics, validation, prediction|
|Research Area:||Computer science and informatics|
|Faculty, School or Research Centre:||Faculty of Science, Engineering and Computing
Faculty of Science, Engineering and Computing > Mobile Information and Network Technologies
Faculty of Science, Engineering and Computing > School of Computing and Information Systems
|Depositing User:||Automatic Import Agent|
|Date Deposited:||15 Dec 2011 11:09|
|Last Modified:||18 Apr 2012 10:27|
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