Title: Gene Shaving using influence function of a kernel method,
Abstract: Identifying significant subsets of the genes, gene shaving is an essential
and challenging issue for biomedical research for a huge number of genes and
the complex nature of biological networks,. Since positive definite kernel
based methods on genomic information can improve the prediction of diseases, in
this paper we proposed a new method, "kernel gene shaving (kernel canonical
correlation analysis (kernel CCA) based gene shaving). This problem is
addressed using the influence function of the kernel CCA. To investigate the
performance of the proposed method in a comparison of three popular gene
selection methods (T-test, SAM and LIMMA), we were used extensive simulated and
real microarray gene expression datasets. The performance measures AUC was
computed for each of the methods. The achievement of the proposed method has
improved than the three well-known gene selection methods. In real data
analysis, the proposed method identified a subsets of $210$ genes out of $2000$
genes. The network of these genes has significantly more interactions than
expected, which indicates that they may function in a concerted effort on colon
cancer.