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Competitive Genomic Screens of Barcoded Yeast Libraries

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

Linear predictive coding and wavelet decomposition for robust microarray data clustering.

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

¹Mobile Information and Network Technologies Research Centre, Kingston University, London, KT1 2EE. 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

|November 16, 2007

Summary

This study compares two genomic signal processing methods, Linear Predictive Coding and Discrete Wavelet Decomposition, for robust microarray data clustering. These methods offer improved accuracy without requiring prior training.

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Area of Science:

Genomics
Digital Signal Processing
Bioinformatics

Background:

Microarray technology enables simultaneous monitoring of gene expression levels.
Clustering techniques are essential for analyzing large-scale gene expression data.
Genomic signal processing integrates genomics with digital signal processing.

Purpose of the Study:

To comparatively analyze Linear Predictive Coding (LPC) and Discrete Wavelet Decomposition (DWD) for microarray data clustering.
To evaluate the robustness and accuracy of these genomic signal processing methods.
To assess the applicability of vector quantization for clustering gene expression data.

Main Methods:

Comparative analysis of Linear Predictive Coding (LPC) and Discrete Wavelet Decomposition (DWD) methods.
Application of vector quantization to the coefficients derived from LPC and DWD.
Validation using standard microarray datasets.

Main Results:

Both LPC and DWD demonstrated improved clustering accuracy for microarray data.
The proposed genomic signal processing methods outperformed conventional clustering techniques.
The developed classifiers do not necessitate any prior training procedures.

Conclusions:

Linear Predictive Coding and Discrete Wavelet Decomposition are effective for robust microarray data clustering.
These methods offer a viable alternative to traditional clustering approaches.
The absence of training requirements enhances the utility of these genomic signal processing techniques.