title: Support Vector Machines with Disease-gene-centric Network Penalty for High Dimensional Microarray Data.
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.
authors.affiliation: Division of Biostatistics, School of Public Health, University of Minnesota.
authors.forename: Yanni
authors.initials: Y
authors.lastname: Zhu
authors.forename: Wei
authors.initials: W
authors.lastname: Pan
authors.forename: Xiaotong
authors.initials: X
authors.lastname: Shen
references.citation: Biometrics. 2010 Jun;66(2):474-84
references.pmid: 19645699
references.citation: Proc Natl Acad Sci U S A. 2007 Jan 16;104(3):955-60
references.pmid: 17215369
references.citation: Nature. 2005 Oct 13;437(7061):1032-7
references.pmid: 16136080
references.citation: Bioinformatics. 2000 Oct;16(10):906-14
references.pmid: 11120680
references.citation: PLoS Genet. 2007 Jun;3(6):e96
references.pmid: 17571924
references.citation: Lancet. 2005 Feb 19-25;365(9460):671-9
references.pmid: 15721472
references.citation: Bioinformatics. 2008 May 1;24(9):1175-82
references.pmid: 18310618
references.citation: Nucleic Acids Res. 2004 Jan 1;32(Database issue):D277-80
references.pmid: 14681412
references.citation: Genes Immun. 2006 Sep;7(6):514-21
references.pmid: 16826235
references.citation: Nucleic Acids Res. 2005 Jan 1;33(Database issue):D418-24
references.pmid: 15608229
references.citation: Hum Mutat. 2003 Mar;21(3):192-200
references.pmid: 12619105
references.citation: Biometrika. 2009 Sep;96(3):513-527
references.pmid: 23946545
references.citation: Nucleic Acids Res. 2004 Jan 1;32(Database issue):D497-501
references.pmid: 14681466
references.citation: Hum Mutat. 2003 Mar;21(3):292-300
references.pmid: 12619115
references.citation: Mol Syst Biol. 2007;3:140
references.pmid: 17940530
references.citation: Proc Natl Acad Sci U S A. 2000 Jan 4;97(1):262-7
references.pmid: 10618406
references.citation: Biometrics. 2008 Mar;64(1):115-23
references.pmid: 17608783
references.citation: BMC Bioinformatics. 2009 Jan 30;10 Suppl 1:S21
references.pmid: 19208121