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Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data

Paper ID Volume ID Publish Year Pages File Format Full-Text
15532 1421 2008 8 PDF Available
Title
Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data
Abstract

Gene expression data are characterized by thousands even tens of thousands of measured genes on only a few tissue samples. This can lead either to possible overfitting and dimensional curse or even to a complete failure in analysis of microarray data. Gene selection is an important component for gene expression-based tumor classification systems. In this paper, we develop a hybrid particle swarm optimization (PSO) and tabu search (HPSOTS) approach for gene selection for tumor classification. The incorporation of tabu search (TS) as a local improvement procedure enables the algorithm HPSOTS to overleap local optima and show satisfactory performance. The proposed approach is applied to three different microarray data sets. Moreover, we compare the performance of HPSOTS on these datasets to that of stepwise selection, the pure TS and PSO algorithm. It has been demonstrated that the HPSOTS is a useful tool for gene selection and mining high dimension data.

Keywords
Particle swarm optimization; Tabu search; Gene selection; Gene expression data
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Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 32, Issue 1, February 2008, Pages 53–60
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us