ANALYSIS OF MACHINE LEARNING TECHNIQUES FOR GENE SELECTION AND CLASSIFICATION OF MICROARRAY DATA.

The rapid developments in the genetics field have generated a huge amount of biological data. Micro array gene expression data is an important instance of biological data. It has high dimensionality with a small number of samples accompanied with large number of genes. Therefore, using machine learning techniques for knowledge discovery in such data become a rich area for researchers. Classification taskdragged a high attention during the analysis ofsuch data, which objective classifying new unseen gene expression data sets into predefined classes. Therefore, implementing an effective gene selection technique and then adjusting a powerful classifier to achieve accurate classification accuracy is a challenge. This paper discusses some of recent research on machine learning approaches for gene expression selection and classification tasks. The paper also, presented a comparative study of these approaches as a first phase of our research motivated by building a newly, promising system for gene expression analysis. The results of the analysis showed that the support vector machine is the most often used classifier of higher classification accuracy. [ABSTRACT FROM AUTHOR]