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Comparative Analysis of Genomic Signal Processing for Microarray Data Clustering
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DOI：10.1109/TNB.2011.2178262.png)
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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.
Keyword:
Discrete wavelet
fractal dimension
genomic signal processing
linear predictive coding
microarray clustering
vector quantization
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4.4
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1.4K
被引数:
2.5K
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