Selection and Classification of Microarray Data: a case study

Abstract

There are a considerable amount of studies on gene selection method for building effective classification models. However, most of the approaches consider solely on gene expression values, and as a result, the selected genes might not be biologically meaningful. We presented an integrative gene selection for improving microarray data classification performance. The proposed method used the corrlation analysis approach to integrate both gene expression and biological data in identifying informative genes. The results demonstrate the validness of the approach.