Incorporating biological information into linear models: A Bayesian approach to the selection of pathways and genes

TL;DR

This paper introduces a Bayesian model that integrates biological pathway information into the analysis of gene expression data, enhancing marker detection and understanding of molecular processes.

Contribution

It presents a novel Bayesian approach that incorporates pathway and gene network data into linear models for gene expression analysis.

Findings

Improved detection of relevant biomarkers.

Enhanced prediction accuracy.

Better understanding of molecular mechanisms.

Abstract

The vast amount of biological knowledge accumulated over the years has allowed researchers to identify various biochemical interactions and define different families of pathways. There is an increased interest in identifying pathways and pathway elements involved in particular biological processes. Drug discovery efforts, for example, are focused on identifying biomarkers as well as pathways related to a disease. We propose a Bayesian model that addresses this question by incorporating information on pathways and gene networks in the analysis of DNA microarray data. Such information is used to define pathway summaries, specify prior distributions, and structure the MCMC moves to fit the model. We illustrate the method with an application to gene expression data with censored survival outcomes. In addition to identifying markers that would have been missed otherwise and improving prediction accuracy, the integration of existing biological knowledge into the analysis provides a better understanding of underlying molecular processes.