Wavelet-based feature extraction for DNA microarray classification

Authors:
Ahmad M. Sarhan
Affiliations:
Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
Venue:
Artificial Intelligence Review
Year:
2013

Citing 4
Cited 1

The nature of statistical learning theory

The nature of statistical learning theory
Support-Vector Networks

Machine Learning
Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data

Bioinformatics
DNA Microarray Classification with Compact Single Hidden-Layer FeedForward Neural Networks

FBIT '07 Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies

Wavelet Analysis in Current Cancer Genome Research: A Survey

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

Quantified Score

Hi-index	0.00

Visualization

Abstract

Complementary DNA (cDNA) microarray-based tumor gene expression profiles have been successfully used for cancer diagnosis. The main difficulty in processing cDNA microarrays is the ultra-high dimensionality of the microarrays. In this paper, we approach the dimensionality reduction using a novel wavelet-based approach that extracts classification features through microarray-block processing, thresholding, and averaging of approximation coefficients. The proposed cancer detection system presents the extracted features to a support vector machine SVM for classification (tumor or non-tumor). To show the robustness of the proposed system, its performance is tested on two public cancer microarray databases.