%0 Journal Article
%T Survival associated pathway identification with group Lp penalized global AUC maximization
%A Zhenqiu Liu
%A Laurence S Magder
%A Terry Hyslop
%A Li Mao
%J Algorithms for Molecular Biology
%D 2010
%I BioMed Central
%R 10.1186/1748-7188-5-30
%X Biologically complex diseases such as cancer are caused by mutations in biological pathways or functional groups instead of individual genes. Statistically, genes sharing the same pathway have high correlations and form functional groups or biological pathways. Many databases about biological knowledge or pathway information are available in the public domain after many years of intensive biomedical research. Such databases are often named metadata, which means data about data. Examples of such databases include the gene ontology (GO) databases (Gene Ontology Consortium, 2001), the Kyoto Encyclopedia of Genes and Genomes (KEGG) database [2], and several other pathways on the internet (e.g., http://www.superarray.com webcite; http://www.biocarta.com webcite). Most current methods, however, are developed purely from computational points without utilizing any prior biological knowledge or information. Gene selections with survival outcome data in the statistical literature are mainly within the penalized Cox or additive risk regression framework [3-8]. The L1 and Lp (p < 1) penalized Cox regressions can work for simultaneous individual gene selection and survival prediction and have been extensively studied in statistics and bioinformatics literature [8-11]. The performance of the survival model is evaluated by the global area under the ROC curve summary (GAUCS) [12]. Unfortunately, those methods are mainly for individual gene selections and cannot be used to identify pathways directly. In microarray analysis, several popular tools for pathway analysis, including GENMAP, CHIPINFO, and GOMINER, are used to identify pathways that are over-expressed by differentially expressed genes. These gene set enrichment analysis (GSEA)methods are very informative and are potentially useful for identifying pathways that related to disease status [13]. One drawback with GSEA is that it considers each pathway separately and the pathway information is not utilized in the modeling stage.
%U http://www.almob.org/content/5/1/30