Title :
Support vector machine classifications for microarray expression data set
Author :
Zhang, Junying ; Lee, Richard ; Wang, Yue Joseph
Author_Institution :
National Key Lab. of Radar Signal Process., Xidian Univ., Xi´´an, China
Abstract :
Gene selection, cancer classification and functional gene classification are three main concerns and interests by biologists for cancer detection, cancer classification, and understanding the functions of genes from the molecular level of tissues, where the large number of genes and relatively small number of experiments in gene expression data generate a great challenge. After a brief introduction of support vector machine(SVM) for classification, this paper presents recent SVM approaches for gene selection, cancer classification and functional gene classification followed by analysis on the advantages and limitations of SVM on these applications.
Keywords :
cancer; genetics; learning (artificial intelligence); pattern recognition; support vector machines; cancer classification; cancer detection; functional gene classification; gene selection; kernel methods; learning algorithm; microarray expression data set; pattern recognition; structural risk minimization; support vector machine; Biology; Cancer detection; Cloning; Computer science; Gene expression; Kernel; Radar signal processing; Signal processing algorithms; Support vector machine classification; Support vector machines;
Conference_Titel :
Computational Intelligence and Multimedia Applications, 2003. ICCIMA 2003. Proceedings. Fifth International Conference on
Print_ISBN :
0-7695-1957-1
DOI :
10.1109/ICCIMA.2003.1238102