Network-based support vector machine for classification of microarray samples

Background: The importance of network-based approach to identifying biological markers for diagnostic classification and prognostic assessment in the context of microarray data has been increasingly recognized. To our knowledge, there have been few, if any, statistical tools that explicitly incorporate the prior information of gene networks into classifier building. The main idea of this paper is to take full advantage of the biological observation that neighboring genes in a network tend to function together in biological processes and to embed this information into a formal statistical framework. Results: We propose a network-based support vector machine for binary classification problems by constructing a penalty term from the F-infinity-norm being applied to pairwise gene neighbors with the hope to improve predictive performance and gene selection. Simulation studies in both low- and high-dimensional data settings as well as two real microarray applications indicate that the proposed method is able to identify more clinically relevant genes while maintaining a sparse model with either similar or higher prediction accuracy compared with the standard and the L-1 penalized support vector machines. Conclusion: The proposed network-based support vector machine has the potential to be a practically useful classification tool for microarrays and other high-dimensional data.