Gene expression data analysis using support vector machines

Author

Chu, Feng ; Wang, Lipo

Author_Institution

Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore

Volume

3

fYear

2003

fDate

20-24 July 2003

Firstpage

2268

Abstract

Cancer classification is an important problem both for clinical treatment and for biomedical research. Considering the good performance of support vector machines (SVMs) on solving pattern recognition problems, we use a C-SVM to process the B-cell lymphoma data. The principal components analysis (PCA) is used for gene selection. A voting scheme is used to do multi-group classification by k(k-1) binary SVMs. The classification results show that SVMs are effective tools for this problem.

Keywords

cancer; data analysis; genetics; patient treatment; pattern classification; principal component analysis; support vector machines; B-cell lymphoma data; SVM; biomedical research; cancer classification; clinical treatment; gene expression data analysis; gene selection; multigroup classification; pattern recognition problems; principal components analysis; support vector machines; Biomedical engineering; Cancer; Data analysis; Electronic mail; Gene expression; Pattern recognition; Principal component analysis; Support vector machine classification; Support vector machines; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2003. Proceedings of the International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-7898-9

Type

conf

DOI

10.1109/IJCNN.2003.1223764

Filename

1223764