Feature selection and classification of gene expression profile in hereditary breast cancer

Correct classification and prediction of tumor cells is essential for successful diagnosis and reliable future treatment. However, it is very challenging to distinguish between tumor classes using microarray with thousands of gene expressions. Removing irrelevant genes is very helpful for us to learn the relationship between genes and tumors. In this paper we have used two methods: multivariate permutation test (MPT) and significant analysis of microarray (SAM) to select significant genes for feature selection. Using those selected features, we applied support vector machine, (SVM) with polynomial, radial and linear kernels, to predict the class of testing data. Our result shows that all the samples are classified correctly. We have achieved 100% accuracy in classification among all the samples with polynomial kernel of SVM while Liner kernel shows no misclassification among BRCA1-BRCA2 and BRCA1-sporadic.