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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Fractal dimension and wavelet decomposition for robust microarray data clustering.

Robert S H Istepanian¹, Ala Sungoor, Jean-Christophe Nebel

¹Mobile Information and Network Technologies Research Centre (MINT), Kingston University, London KT1 2EE, UK. r.istepanian@kingston.ac.uk

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 24, 2009

Summary

This study compares two genomic signal processing methods for microarray data clustering. These novel techniques, Fractal Dimension and Discrete Wavelet Decomposition, offer improved accuracy without prior training.

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Area of Science:

Genomics
Digital Signal Processing
Bioinformatics

Background:

Microarrays are crucial for gene expression analysis.
Clustering techniques are commonly used for microarray data interpretation.
Genomic signal processing integrates genomics with digital signal processing.

Purpose of the Study:

To comparatively analyze two genomic signal processing methods for robust microarray data clustering.
To evaluate the effectiveness of Fractal Dimension and Discrete Wavelet Decomposition with Vector Quantization.
To assess the performance against conventional clustering techniques.

Main Methods:

Fractal Dimension-based genomic signal processing.
Discrete Wavelet Decomposition with Vector Quantization for genomic data.
Validation using standard microarray datasets.

Main Results:

Both Fractal Dimension and Discrete Wavelet Decomposition methods demonstrated improved clustering accuracy.
These methods outperformed certain conventional clustering techniques.
The proposed classifiers do not require prior training procedures.

Conclusions:

Genomic signal processing offers advanced solutions for microarray data analysis.
Fractal Dimension and Discrete Wavelet Decomposition are effective for robust and accurate microarray clustering.
The unsupervised nature of these methods simplifies their application in gene expression studies.