A Bayesian Approach for Graph-constrained Estimation for High-dimensional Regression.

ABSTRACT: Many different biological processes are represented by network graphs such as regulatory networks, metabolic pathways, and protein-protein interaction networks. Since genes that are linked on the networks usually have biologically similar functions, the linked genes form molecular modules to affect the clinical phenotypes/outcomes. Similarly, in large-scale genetic association studies, many SNPs are in high linkage disequilibrium (LD), which can also be summarized as a LD graph. In order to incorporate the graph information into regression analysis with high dimensional genomic data as predictors, we introduce a Bayesian approach for graph-constrained estimation (Bayesian GRACE) and regularization, which controls the amount of regularization for sparsity and smoothness of the regression co

SUBMITTER: Sun H 

PROVIDER: S-EPMC4373540 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature