文章基本信息

标题：Support vector machines with disease-gene-centric network penalty for high dimensional microarray data
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作者：Wei Pan ; Xiaotong Shen ; Yanni Zhu 等
期刊名称：Statistics and Its Interface
印刷版ISSN：1938-7989
电子版ISSN：1938-7997
出版年度：2009
卷号：2
期号：3
页码：257-269
DOI：10.4310/SII.2009.v2.n3.a1
出版社：International Press
摘要：With the availability of gene 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. DGC-SVM aims to detect genes clustering together and around some key disease genes in a gene network. Toward this end, 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 $L_{\infty}$-norm over 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 while retaining predictive performance of 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.
关键词：DAG; gene expression; gene network; grouped penalty; hierarchy; penalization