关键词: 支持向量机 基因表达谱 肿瘤分类 主成份分析

Abstract:

The tumor diagnosis method based on gene expression profiles will be developed into a fast and effective method in clinical domain in the near future.Although DNA microarray experiments provide us with a huge amount of gene expression data, in fact, only a few genes relate to tumor. Moreover, it is d  ifficult to extract tumor-related genes from gene expression profiles because of its characteristics such as the high dimensionality, the small sample set, many noises and redundancies in gene expression profiles. In this paper we propose a novel feature extraction approach which projects high dimensional data onto a lower dimensional feature space,which improves the SVM-based classification performance of gene expression data. We have examined two sets of gene expression data （colon dataset and leukemia dataset） by means of SVM classifiers with different parameters to validate the proposed approachh. Experimental results show that SVM has a superior performance in the classification of gene expression data using the principal components extracted  from the top-ranked genes based on the gene ranking method. The cross-validation accuracy of 95.16% has been achieved for colon dataset using SVM classi   fiers and 100% for leukemia dataset also. Another advantage of the proposed method is that the results of the sample classification can be visualized in  the form of 2D or 3D scatter plot.

Key words: （SVM;genc expression profile, tumor classification;principal component analysis）