Network Kernel SVM for microarray classification and gene sets selection

The importance of network-based approach to identifying biological markers has been increasingly recognized. Lots of papers indicated that genes in a network tend to function together in biological processes, so taking full advantage of the biological observation can improve the performance of microarray classification. However, lots of SVM methods don´t consider this situation during their classifier building. The main idea of this paper intends to embed the information of gene networks into a new SVM learning framework. Based on a new regularization, we propose a novel method, Network Kernel SVM (NK-SVM), for binary classification problem and gene sets selection. By constructing some special kernel matrixes from the prior information of gene network, the new NK-SVM method makes the genes in the same set to be selected (or eliminated) together. The numerical experiments on a real microarray application show that the proposed method tends to provide a better performance than other methods on gene sets selection.