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. 2012 Jan;19(1):68-82.
doi: 10.1089/cmb.2010.0064. Epub 2011 Jan 6.

A hybrid BPSO-CGA approach for gene selection and classification of microarray data

Li-Yeh Chuang  1 , Cheng-Huei Yang, Jung-Chike Li, Cheng-Hong Yang
Affiliations

A hybrid BPSO-CGA approach for gene selection and classification of microarray data

Li-Yeh Chuang et al. J Comput Biol. 2012 Jan.

Abstract

Microarray analysis promises to detect variations in gene expressions, and changes in the transcription rates of an entire genome in vivo. Microarray gene expression profiles indicate the relative abundance of mRNA corresponding to the genes. The selection of relevant genes from microarray data poses a formidable challenge to researchers due to the high-dimensionality of features, multiclass categories being involved, and the usually small sample size. A classification process is often employed which decreases the dimensionality of the microarray data. In order to correctly analyze microarray data, the goal is to find an optimal subset of features (genes) which adequately represents the original set of features. A hybrid method of binary particle swarm optimization (BPSO) and a combat genetic algorithm (CGA) is to perform the microarray data selection. The K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) served as a classifier. The proposed BPSO-CGA approach is compared to ten microarray data sets from the literature. The experimental results indicate that the proposed method not only effectively reduce the number of genes expression level, but also achieves a low classification error rate.

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Figures

FIG. 1.
FIG. 1.
Flowchart of the hybrid BPSO-CG.
FIG. 2.
FIG. 2.
Improved percentage classification error rates in ten microarray data with BPSO-CGA method.
FIG. 3.
FIG. 3.
The classification error rate of NCI60 microarray data in six different methods.
FIG. 4.1.
FIG. 4.1.
Number of iterations versus classification error rate in Leukemia.
FIG. 4.2.
FIG. 4.2.
Number of iterations versus classification error rate in Breast 2 class.
FIG. 4.3.
FIG. 4.3.
Number of iterations versus classification error rate in Breast 3 class.
FIG. 4.4.
FIG. 4.4.
Number of iterations versus classification error rate in NCI60.
FIG. 4.5.
FIG. 4.5.
Number of iterations versus classification error rate in Adenocarcinoma.
FIG. 4.6.
FIG. 4.6.
Number of iterations versus classification error rate in Colon.
FIG. 4.7.
FIG. 4.7.
Number of iterations versus classification error rate in Brain.
FIG. 4.8.
FIG. 4.8.
Number of iterations versus classification error rate in Lymphoma.
FIG. 4.9.
FIG. 4.9.
Number of iterations versus classification error rate in Prostate.
FIG. 4.10.
FIG. 4.10.
Number of iterations versus classification error rate in Srbct.
See this image and copyright information in PMC

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