Incorporating predictor network in penalized regression with application to microarray data.

ABSTRACT: We consider penalized linear regression, especially for "large p, small n" problems, for which the relationships among predictors are described a priori by a network. A class of motivating examples includes modeling a phenotype through gene expression profiles while accounting for coordinated functioning of genes in the form of biological pathways or networks. To incorporate the prior knowledge of the similar effect sizes of neighboring predictors in a network, we propose a grouped penalty based on the L(gamma)-norm that smoothes the regression coefficients of the predictors over the network. The main feature of the proposed method is its ability to automatically realize grouped variable selection and exploit grouping effects. We also discuss effects of the choices of the gamma and some we

SUBMITTER: Pan W 

PROVIDER: S-EPMC3338337 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature