DNA microarray data analysis using nonlinear classification procedures

Abstract

DNA microarray data analysis is one of the core technologies promoting genome research. By measuring expression levels of more than 10,000 genes, DNA microarray analysis enables us to analyze the relationshipas between gene expression and behavior in various biological phenomena such as development, differentiation, growth, canceration, aging and so on. The applications of DNA microarray data analysis have increased in the post-genome age in which the function analyses of genes are more important and a number of multivariate DNA microarray data sets have been produced. Since the dimension of the data set is too high and their interpretations as well as comparisons are too difficult, the methods for analyzing the data set have not been established yet.Under these circumstances, the necessity of the method which enables us to classify or interpret the data set is increasing naturally. In this study, we applied nonlinear classification procedures, SOM and kernel PCA, to the DNA microarray data analysis of human lung carcinoma. As a result, we found 82 genes which were estimated to be associated with carcinogenesis from 12,600 genes.