Analysis of SAGE Results with Combined Learning Techniques

Serial analysis of gene expression (SAGE) experiments could provide us the expression level of thousands of genes in a biological sample. However, the number of available samples is relatively small. Such undersampled problem needs to be carefully analyzed from a ma- chine learning perspective. In this paper, we combine several state-of-the- art techniques for classication, feature selection, and error estimation, and evaluate the performance of the combined techniques on the SAGE dataset. Our results show that a novel algorithm, support vector ma- chine with the stump kernel, performs well on the SAGE dataset both for building an accurate classier, and for selecting relevant features.

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