Cancer classification through feature selection and transductive SVM using gene microarray data

Abstract

With the advancement of microarray technology, gene expression profiling has shown great potential in outcome prediction for different types of cancers. They are also useful for identifying potential gene markers for each cancer subtype, which helps in successful diagnosis of particular cancer types. Traditional supervised classifiers can only work with labeled data. Consequently, a large number of microarray data that do not have adequate follow-up information are disregarded. A Novel approach to combine feature (gene) selection and transductive SVM (TSVM) has been proposed. The selected genes of the microarray data are then exploited to design the transductive SVM. Experimental results confirm the effectiveness of the proposed method in the area of semisupervised cancer classification as well as gene marker identification.