A Bayesian approach for inducing sparsity in generalized linear models with multi-category response.

ABSTRACT:

Background

The dimension and complexity of high-throughput gene expression data create many challenges for downstream analysis. Several approaches exist to reduce the number of variables with respect to small sample sizes. In this study, we utilized the Generalized Double Pareto (GDP) prior to induce sparsity in a Bayesian Generalized Linear Model (GLM) setting. The approach was evaluated using a publicly available microarray dataset containing 99 samples corresponding to four different prostate cancer subtypes.

Results

A hierarchical Sparse Bayesian GLM using GDP prior (SBGG) was developed to take into account the progressive nature of the response variable. We obtained an average overall classification accuracy between 82.5% and 94%, which was higher than Support Vector Ma

SUBMITTER: Madahian B 

PROVIDER: S-EPMC4597416 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature