Classification of Ovarian Cancer based on Intelligent Systems with Microarray Data

Author

Jeng, Jin-Tsong ; Lee, Tsu-Tian ; Lee, Yung-Cheng

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Nat. Formosa Univ., Yunlin

Volume

2

fYear

2005

fDate

12-12 Oct. 2005

Firstpage

1053

Lastpage

1058

Abstract

This paper studies an intelligent system, including a support vector regression (SVR) and a similar analysis, for the classification of the ovarian cancer with microarray data. That is, steps in the classification include a feature selection step and a distance measure step. Firstly, the SVR is used to do the feature selection. That is, the SVR is applied to obtain the important genes of ovarian cancer for all samples of microarray data. At the same time, we can compute the frequency of the gene selection based on the results of SVR for all samples to determine the target genes of ovarian cancer. Secondly, the distance under the similar analysis between target data and test data can be determined. From the distance results, the classification of ovarian cancer can easy to determine

Keywords

cancer; genetics; knowledge based systems; medical computing; pattern classification; regression analysis; support vector machines; SVR; distance measure; feature selection; intelligent system; microarray data classification; ovarian cancer data classification; ovarian cancer gene selection; support vector regression; Cancer; Computer science; Data analysis; Electronic mail; Gene expression; Intelligent systems; Machine learning; Pattern analysis; Support vector machine classification; Support vector machines; Classification; Intelligent Systems; Microarray Data; Ovarian Cancer; Similar Analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2005 IEEE International Conference on

Conference_Location

Waikoloa, HI

Print_ISBN

0-7803-9298-1

Type

conf

DOI

10.1109/ICSMC.2005.1571285

Filename

1571285