Fractal dimension and wavelet decomposition for robust microarray data clustering

Microarrays are now established technologies which are considered as key to gene expression analysis. Their study 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...

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Published in:2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2008; pp. 4106 - 4109
Main Authors: Istepanian, Robert S. H., Sungoor, Ala, Nebel, Jean-Christophe
Format: Conference Proceeding Journal Article
Language:English
Published: United States IEEE 01.01.2008
Subjects:
Algorithms
Bioinformatics
Cluster Analysis
Computers
Correlation
Discrete wavelet transforms
Distortion
Distortion measurement
Fractals
Genetic Vectors
Genome
Genomics
Genomics - methods
Humans
Models, Statistical
Oligonucleotide Array Sequence Analysis - methods
Reproducibility of Results
Signal Processing, Computer-Assisted
Software
Vector quantization
Vectors
Wavelet transforms
ISBN:9781424418145, 1424418143
ISSN:1094-687X, 1557-170X, 2375-7477
Online Access:Get full text
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Summary:Microarrays are now established technologies which are considered as key to gene expression analysis. Their study 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 for robust microarray data clustering. Techniques based on Fractal Dimension and Discrete Wavelet Decomposition with Vector Quantization are validated for standard data sets. Comparative analysis of the results indicates that these methods provide improved clustering accuracy compared to some conventional clustering techniques. Moreover, these classifiers don't require any prior training procedures
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ISBN:9781424418145
1424418143
ISSN:1094-687X
1557-170X
2375-7477
DOI:10.1109/IEMBS.2008.4650112