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• W2136628047 abstract "With the availability of genetic pathways or networks and accumulating knowledge on genes with variants predisposing to diseases (disease genes), we propose a disease-gene-centric support vector machine (DGC-SVM) that directly incorporates these two sources of prior information into building microarray-based classifiers for binary classification problems. DGC-SVM aims to detect the genes clustering together and around some key disease genes in a gene network. To achieve this goal, we propose a penalty over suitably defined groups of genes. A hierarchy is imposed on an undirected gene network to facilitate the definition of such gene groups. Our proposed DGC-SVM utilizes the hinge loss penalized by a sum of the L(infinity)-norm being applied to each group. The simulation studies show that DGC-SVM not only detects more disease genes along pathways than the existing standard SVM and SVM with an L(1)-penalty (L1-SVM), but also captures disease genes that potentially affect the outcome only weakly. Two real data applications demonstrate that DGC-SVM improves gene selection with predictive performance comparable to the standard-SVM and L1-SVM. The proposed method has the potential to be an effective classification tool that encourages gene selection along paths to or clustering around known disease genes for microarray data." @default.
• W2136628047 created "2016-06-24" @default.
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• W2136628047 date "2009-01-01" @default.
• W2136628047 modified "2023-10-01" @default.
• W2136628047 title "Support vector machines with disease-gene-centric network penalty for high dimensional microarray data" @default.
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• W2136628047 doi "https://doi.org/10.4310/sii.2009.v2.n3.a1" @default.
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