Feature Selection of Gene Expression Data Using Regression Model

There have been a lot of researches that demonstrate the phenomenon of life or the origin of the disease and classify or diagnose the state of the cell. These are usually achieved by the strength of the gene expression under certain circumstances by the microarray which can observe tens and thousands of gene expression profile. It is not feasible to use all the attributes because a lots of gene expression data are involved in microarray experiments. Therefore, in order to select the significant genes from lots of data, we applied the hybrid method combining filter method with LASSO model. As experimental data set, leukemia data are applied to a number of classifiers such as naïve Bayesian, SVM, Bayesian network, logistic regression and random forest. In the experimental result, we found that the gene selection method using the LASSO outperforms the existing gene selection method.