Feature selection and classification of microarray gene expression data of ovarian carcinoma patients using weighted voting support vector machine

We can reach by DNA microarray gene expression to such wealth of information with thousands of variables (genes). Analysis of this information can show genetic reasons of disease and tumor differences. In this study we try to reduce high-dimensional data by statistical method to select valuable genes with high impact as biomarkers and then classify ovarian tumor based on gene expression data of two patient groups. One group treated by standard therapies and survived, while another group didn’t be cure and die after some times. In the first step we used weighted voting algorithm (WVA) for selecting impressive genes to reduce dimension, therefore eliminate noisy data and make analysis easier and then partial least square – discriminante analysis (PLS-DA) and support vector machine (SVM) methods have been applied for classification of diminished data. Results show that classification by PLS-DA can distinguish two groups somewhat but SVM is more efficient and sufficient classification method.