Browse > Article
http://dx.doi.org/10.6109/jkiice.2008.12.12.2259

The Design Of Microarray Classification System Using Combination Of Significant Gene Selection Method Based On Normalization.  

Park, Su-Young (조선대학교 컴퓨터통계학과)
Jung, Chai-Yeoung (조선대학교 컴퓨터통계학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.12, no.12, 2008 , pp. 2259-2264 More about this Journal
Abstract
Significant genes are defined as genes in which the expression level characterizes a specific experimental condition. Such genes in which the expression levels differ significantly between different groups are highly informative relevant to the studied phenomenon. In this paper, first the system can detect informative genes by similarity scale combination method being proposed in this paper after normalizing data with methods that are the most widely used among several normalization methods proposed the while. And it compare and analyze a performance of each of normalization methods with multi-perceptron neural network layer. The Result classifying in Multi-Perceptron neural network classifier for selected 200 genes using combination of PC(Pearson correlation coefficient) and ED(Euclidean distance coefficient) after Lowess normalization represented the improved classification performance of 98.84%.
Keywords
Lowess normalization; PC-ED combination method; MLP(multi-Layer perceptron);
Citations & Related Records
연도 인용수 순위
  • Reference
1 S. Dudoit, J. Fridlyand, and T. P. Speed, "Comparison of discrimination methods for the classification of tumors using gene expression data", Journal of the American Statistical Association, vol. 97, pp. 77-87, 2002   DOI   ScienceOn
2 M. Brown, W. Grundy, D. Lin, N. Christianini, C. Sugnet, M. Ares Jr., and D. Haussler, "Support vector machine classification of microarray gene expression data", UCSC-CRL 99-09, Department of Computer Science, University California Santa Cruz, Santa Cruz, CA, June, 1999
3 Golub, T.R., Slonim, D.K, Tamayo, P., Huard, D., Gaasenbeek, M., Mesirov, J.P., Collrt, H., Loh, M.L.Downing, J.R, Caligiuri, M.A., Bloomfield, D.D., and Lander, E.S., "Molecular classification of cancer: class discovery and class prediction by gene expression monitoring", Science, vol. 286, no. 5439, pp. 531-537, 1999   DOI   ScienceOn
4 Dov Stekel, Microarray Bioinformatics, Cambridge University Press, 2003