Gene selection for breast cancer

Abstract

Breast cancer can be fatal and so it is very dangerous. Early diagnosis of breast cancer has been playing very important role on treatment of the disease. Recently, gene technology has been widely used in cancer diagnosis. A microarray is a tool for analyzing gene expression. Microarray data usually contain thousands of genes and a small number of samples. Although, most of them are irrelevant or insignificant to a clinical diagnosis. It is very difficult to obtain a satisfactory classification result by machine learning techniques because of both the curse-of dimensionality problem and the overfitting problem. Therefore, feature selection plays a crucial role in microarray analysis. In this paper, significant biomarker genes for diagnosis have been identified by feature selection. We attempted to use these markers for the classification of breast cancer. Subsequently, SVM was also used to verify the classification rate of genes selected by feature selection. The classification rate of SVM reaches to 82.69% when using selected genes.