Comparative Analysis of Genomic Signal Processing for Microarray Data Clustering

被引：7

作者：

Istepanian, Robert S. H. ^{[1
]}

Sungoor, Ala ^{[1
]}

Nebel, Jean-Christophe ^{[2
]}

机构：

[1] Kingston Univ London, Mobile Informat & Network Technol Res Ctr, Kingston Upon Thames KT1 2EE, Surrey, England

[2] Kingston Univ London, Fac Sci Engn & Comp, Kingston Upon Thames KT1 2EE, Surrey, England

来源：

IEEE TRANSACTIONS ON NANOBIOSCIENCE | 2011年 / 10卷 / 04期

关键词：

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;

D O I：

10.1109/TNB.2011.2178262

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页码：225 / 238

页数：14

共 46 条

[1] Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays [J].