Analyzing Microarray Data with Classification and Clustering Methods

Gene-expression microarrays, commonly called gene chips, make it possible to simultaneously measure the rate at which a cell or tissue is expressing-translating into a protein-each of its thousands of genes. One can use these comprehensive snapshots of biological activity to infer regulatory pathways in cells, identify novel targets for drug design, and improve the diagnosis, prognosis, and treatment planning for those suffering from disease. However, the amount of data this new technology produces is more than one can manually analyze. Hence, the need for automated analysis of microarray data offers an opportunity to have a significant impact on biology and medicine. We present the comparison of different classification and clustering methods to learn the best model from the microarray data and use it to predict disease outcomes. We also explain how to apply clustering and classification methods on gene expression data. These methods have become very popular and are implemented in freely available software in order to predict the participation of gene products in a specific functional category of interest.