Integrative gene selection for classification of microarray data

Microarray data classification is one of the major interests in health informatics that aims at discovering hidden patterns in gene expression profiles. The main challenge in building this classification system is the curse of dimensionality problem. Thus, there is 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. This paper presents an integrative gene selection for improving microarray data classification performance. The proposed approach employs the association analysis technique to integrate both gene expression and biological data in identifying informative genes. The experimental results show that the proposed gene selection outperformed the traditional method in terms of accuracy and number of selected genes.

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