Machine Learning Techniques and Chi-Square Feature Selection for Cancer Classification Using SAGE Gene Expression Profiles

Abstract

Recently developed Serial Analysis of Gene Expression (SAGE) technology enables us to simultaneously quantify the expression levels of tens of thousands of genes in a population of cells. SAGE is better than Microarray in that SAGE can monitor both known and unknown genes while Microarray can only measure known genes. SAGE gene expression profiling based cancer classification is a better choice since cancers may be due to some unknown genes. Whereas a wide range of methods has been applied to traditional Microarray based cancer classification, relatively few studies have been done on SAGE based cancer classification. In our study we evaluate popular machine learning methods (SVM, Naive Bayes, Nearest Neighbor, C4.5 and RIPPER) for classifying cancers based on SAGE data. In order to deal with the high dimensional problem, we propose to use Chi-square for tag/gene selection. Both binary classification and multicategory classification are investigated. The experiments are based on two human SAGE datasets: brain and breast. The results show that SVM and Naive Bayes are the top-performing SAGE classifiers and that Chi-square based gene selection can improve the performance of all the five classifiers investigated.