Gene selection and classification rule generation for microarray dataset

Abstract

Microarray is a useful technique for measuring expression data of thousands or more of genes simultaneously. One of challenges in classification of cancer using high-dimensional gene expression data is to select a minimal number of relevant genes which can maximize classification accuracy. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust gene identification methods is extremely fundamental. Many gene selection methods as well as their corresponding classifiers have been proposed. In the proposed method, a single gene with high class-discrimination capability is selected and classification rules are generated for cancer based on gene expression profiles.