title: Gene identification and survival prediction with Lp Cox regression and novel similarity measure.
abstract: In this paper, Cox's proportional hazards model with Lp penalty method is developed for simultaneous gene selection and survival prediction. Lp penalty shrinks coefficients and produces some coefficients that are exactly zero, and therefore can be used to identify survival related downstream genes. We also define a novel similarity measure to hunt the regulatory genes that their gene expression changes may be low but they are highly correlated with the selected genes. Experimental results with gene expression data demonstrate that the proposed procedures can be used for identifying important gene clusters that are related to time to death due to cancer and for building parsimonious model for predicting the survival of future patients.
authors.affiliation: University of Maryland Greenebaum Cancer Center, Baltimore, MD 21201, USA. zliu@umm.edu
authors.forename: Zhenqiu
authors.initials: Z
authors.lastname: Liu
authors.forename: Feng
authors.initials: F
authors.lastname: Jiang
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D052138:Genes, Neoplasm; D006801:Humans; D009369:Neoplasms / Q000401:mortality*; D020411:Oligonucleotide Array Sequence Analysis; D016016:Proportional Hazards Models; D016019:Survival Analysis