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Identifying significant subsets of the genes, gene shaving is an essentialand challenging issue for biomedical research for a huge number of genes andthe complex nature of biological networks,. Since positive definite kernelbased methods on genomic information can improve the prediction of diseases, inthis paper we proposed a new method, "kernel gene shaving (kernel canonicalcorrelation analysis (kernel CCA) based gene shaving). This problem isaddressed using the influence function of the kernel CCA. To investigate theperformance of the proposed method in a comparison of three popular geneselection methods (T-test, SAM and LIMMA), we were used extensive simulated andreal microarray gene expression datasets. The performance measures AUC wascomputed for each of the methods. The achievement of the proposed method hasimproved than the three well-known gene selection methods. In real dataanalysis, the proposed method identified a subsets of $210$ genes out of $2000$genes. The network of these genes has significantly more interactions thanexpected, which indicates that they may function in a concerted effort on coloncancer.

Gene Shaving using influence function of a kernel method