A model for gene selection and classification of gene expression data
- 95 Downloads
Gene expression data are expected to be of significant help in the development of efficient cancer diagnosis and classification platforms. One problem arising from these data is how to select a small subset of genes from thousands of genes and a few samples that are inherently noisy. This research aims to select a small subset of informative genes from the gene expression data which will maximize the classification accuracy. A model for gene selection and classification has been developed by using a filter approach, and an improved hybrid of the genetic algorithm and a support vector machine classifier. We show that the classification accuracy of the proposed model is useful for the cancer classification of one widely used gene expression benchmark data set.
Key wordsGene selection Hybrid approach Filter approach Gene expression data
Unable to display preview. Download preview PDF.
- 5.Ryu J, Cho SB (2002) Towards optimal feature and classifier for gene expression classification of cancer. Proceedings of the 2002 AFSS International Conference on Fuzzy Systems (AFSS2002), 2002, Calcutta, India, LNCS 2275 Springer-Verlag, London, UK, pp 310–317Google Scholar
- 7.Wang Y, Makedon F (2004) Application of Relief-F feature filtering algorithm to selecting informative genes for cancer classification using microarray data. Proceedings of the IEEE Conference on Computational Systems Bioinformatics (CSB'04), 2004, IEEE Press, Stanford, CA, USA, pp 497–498Google Scholar