Support Vector Machines and Neural Networks as Marker Selectors for Cancer Gene Analysis

Author

Blazadonakis, M.E. ; Zervakis, M. ; Kounelakis, M. ; Biganzoli, E. ; Lama, N.

Author_Institution

Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete

fYear

2006

fDate

Sept. 2006

Firstpage

626

Lastpage

631

Abstract

DNA micro-array analysis allows us to study the expression level of thousands of genes simultaneously on a single experiment. The problem of marker selection has been extensively studied but in this paper we also consider the quality of the selected markers. Thus, we address the problem of selecting a small subset of genes that would be adequate enough to discriminate between the two classes of interest in classification, while preserving self-similar characteristics to allow closed clustering within each class

Keywords

DNA; biology computing; cancer; genetics; neural nets; pattern classification; pattern clustering; support vector machines; DNA microarray analysis; cancer classification; cancer gene analysis; closed clustering; marker selection; marker selector; neural network; support vector machine; Cancer; DNA; Intelligent networks; Intelligent systems; Monitoring; Neural networks; Pathology; Proposals; Support vector machines; System testing; DNA micro-array; cancer classification; gene selection; marker selection;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems, 2006 3rd International IEEE Conference on

Conference_Location

London

Print_ISBN

1-4244-01996-8

Electronic_ISBN

1-4244-01996-8

Type

conf

DOI

10.1109/IS.2006.348492

Filename

4155499