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Analysis of SAGE Results with Combined Learning Techniques
 

Summary: Analysis of SAGE Results with Combined
Learning Techniques
Hsuan­Tien Lin and Ling Li
Learning Systems Group, California Institute of Technology, USA
htlin@caltech.edu, ling@caltech.edu
Abstract. 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 state­of­the­
art techniques for classification, 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 classifier, and for selecting relevant features.
1 Introduction
Serial analysis of gene expression (SAGE) experiments could provide us an enor­
mous amount of information on the expression level of di#erent genes in some
cell populations. The expression level is evaluated by counting the occurrences
of the SAGE tags that can identify an unique transcript [1].

  

Source: Abu-Mostafa, Yaser S. - Department of Mechanical Engineering & Computer Science Department, California Institute of Technology

 

Collections: Computer Technologies and Information Sciences