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

Authors:
Qi Shen;Wei-Min Shi;Wei Kong
Affiliations:
Chemistry Department, Zhengzhou University, Zhengzhou 450052, China;Chemistry Department, Zhengzhou University, Zhengzhou 450052, China;Chemistry Department, Zhengzhou University, Zhengzhou 450052, China
Venue:
Computational Biology and Chemistry
Year:
2008

Citing 8
Cited 1

Future paths for integer programming and links to artificial intelligence

Computers and Operations Research - Special issue: Applications of integer programming
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression

Bioinformatics
HykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data

Bioinformatics
What should be expected from feature selection in small-sample settings

Bioinformatics
The-more-the-better and the-less-the-better

Bioinformatics
The particle swarm - explosion, stability, and convergence in amultidimensional complex space

IEEE Transactions on Evolutionary Computation
Multi-class tumor classification by discriminant partial least squares using microarray gene expression data and assessment of classification models

Computational Biology and Chemistry

A Complementary Cyber Swarm Algorithm

International Journal of Swarm Intelligence Research

Quantified Score

Hi-index	0.00

Visualization

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.