DocumentCode :
3047622
Title :
Gene Selection and Tissue Classification Based on Support Vector Machine and Genetic Algorithm
Author :
Li, Shutao ; Tan, Mingkui
Author_Institution :
Coll. of Electr. & Inf. Eng., Hunan Univ., Changsha
fYear :
2007
fDate :
6-8 July 2007
Firstpage :
192
Lastpage :
195
Abstract :
The main problems in tissue classification by using DNA Microarray data are selecting genes relevant for a given tumor and constructing the optimized classifiers. This paper proposes a new gene selection and tissue classification method based on support vector machines and genetic algorithm. Firstly, the Wilcoxon-test is used as a coarse gene selection method to remove most of the irrelevant genes. Then the fine selection on the basis of its classification ability of a single gene with support vector machine is conducted to get the final gene subset. Finally, genetic algorithm is used to optimize the parameters of support vector machine to find out the best parameters with the gene subset. The experimental results with the Leukemia and breast cancer dataset show that the proposed strategy is more effective and competitive to the previous methods.
Keywords :
DNA; biological tissues; genetic algorithms; genetics; medical computing; support vector machines; tumours; DNA microarray data; Wilcoxon test; breast cancer dataset; coarse gene selection method; genetic algorithm; leukemia dataset; support vector machine; tissue classification; tumor; Accuracy; Breast cancer; DNA; Data engineering; Educational institutions; Genetic algorithms; Support vector machine classification; Support vector machines; Testing; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedical Engineering, 2007. ICBBE 2007. The 1st International Conference on
Conference_Location :
Wuhan
Print_ISBN :
1-4244-1120-3
Type :
conf
DOI :
10.1109/ICBBE.2007.52
Filename :
4272536
Link To Document :
بازگشت