Group spike-and-slab lasso generalized linear models for disease prediction and associated genes detection by incorporating pathway information.

ABSTRACT:

Motivation

Large-scale molecular data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, standard approaches for omics data analysis ignore the group structure among genes encoded in functional relationships or pathway information.

Results

We propose new Bayesian hierarchical generalized linear models, called group spike-and-slab lasso GLMs, for predicting disease outcomes and detecting associated genes by incorporating large-scale molecular data and group structures. The proposed model employs a mixture double-exponential prior for coefficients that induces self-adaptive shrinkage amount on different coefficients. The group information is incorporated into the model by setting group-specific

SUBMITTER: Tang Z 

PROVIDER: S-EPMC5860634 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature