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A gene selection method for classifying cancer samples using 1D discrete wavelet transform.

Adarsh Jose1, Dale Mugler, Zhong-Hui Duan

  • 1Department of Biomedical Engineering, University of Akron, Akron, OH 44236, USA. adarshjos@gmail.com

International Journal of Computational Biology and Drug Design
|January 22, 2010
PubMed
Summary
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This study introduces a novel wavelet-based feature selection method for DNA microarray analysis. This approach effectively identifies discriminant genes, enhancing biological sample classification accuracy.

Area of Science:

  • Bioinformatics
  • Signal Processing
  • Gene Expression Analysis

Background:

  • DNA microarray data analysis requires effective feature selection for accurate biological sample classification.
  • Wavelet transform, a signal processing tool, has underutilized potential in gene expression data analysis.

Purpose of the Study:

  • To present a novel wavelet-based feature selection method for differentiating biological samples based on DNA microarray data.
  • To evaluate the efficacy of this method in identifying discriminant genes for classification.

Main Methods:

  • Decomposition of gene expression signals using multi-level wavelet transform.
  • Scoring genes based on their discriminative power for two-class sample differentiation.
  • Selection of top-scoring genes to form feature sets.

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  • Coupling selected feature sets with k-nearest neighbour (kNN) classifiers for performance assessment.

    • Main Results:

    • The wavelet-based feature selection method successfully identified discriminant genes.
    • Classification accuracies were evaluated using real DNA microarray datasets.
    • Performance was compared against established feature selection techniques, demonstrating competitive or superior results.

    Conclusions:

    • 1D wavelet analysis is a valuable and effective tool for analyzing gene expression patterns.
    • The proposed wavelet-based method offers a promising approach for feature selection in DNA microarray studies.
    • This technique can improve the efficiency and accuracy of biological sample classification.