Genetic algorithm and support vector machine-based gene microarray analysis
10.3969/j.issn.1673-8225.2010.17.015
- VernacularTitle:基于遗传算法与支持向量机的基因微阵列分析
- Author:
Wei WANG
;
Hong LIU
- Publication Type:Journal Article
- From:
Chinese Journal of Tissue Engineering Research
2010;14(17):3099-3103
- CountryChina
- Language:Chinese
-
Abstract:
BACKGROUND: Gene microarray data has small sample size and large numbers of variates.Traditional statistical method is not effective.Genetic algorithm(GA)and support vector machine(SVM)are machine learning algorithms developed rapidly in recent years,which can decrease the dimension of features.OBJECTIVE: To combine GA and SVM to classify samples and compare with other two processes in which all genes and difference expression genes are taken as classifiers,respectively.METHODS: We applied golub data set provided by Bioconductor,which included gene expression data of leukaemia samples and normal samples.All genes were used to classify samples with SVM.SAM software was used to extract difference expression genes and estimate False Discovery Rate.Finally,76 difference expression genes were used as feature gene set to classify samples with SVM and GA-SVM respectively.Three classification effects were compared.Additionally,the distribution and function about feature genes in KEGG pathways were also discussed.RESULTS AND CONCLUSION: The accuracy of classification of SVM was improved by decreasing dimension with genetic algorithm.In particular,this process eliminated a great deal of redundant genes and noises,which improves the classification performance.Results show that GA-SVM algorithm is effective in classifying samples.In addition,the pathway analysis shows that signal transmission and amino acid metabolism are two major functions of feature genes.
