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Joint Bayesian variable and graph selection for regression models with network-structured predictors.

Christine B Peterson1, Francesco C Stingo2, Marina Vannucci3

  • 1Department of Health Research and Policy, Stanford University, Stanford, CA, 94305, U.S.A.

Statistics in Medicine
|October 31, 2015
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Summary
This summary is machine-generated.

This study introduces a Bayesian method for selecting linked predictors by integrating sparse regression and graphical models. It effectively identifies gene or protein pathways impacting outcomes, outperforming existing network-guided variable selection techniques.

Keywords:
Bayesian variable selectionGaussian graphical modellinear modelprotein network

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Predictor selection is crucial in high-dimensional data, especially in genomics.
  • Existing network-guided methods often require prior network information.
  • Identifying functional relationships among predictors is key to understanding biological systems.

Purpose of the Study:

  • To develop a Bayesian approach for network-informed predictor selection.
  • To infer predictor networks without prior knowledge.
  • To identify pathways of functionally related genes/proteins impacting an outcome.

Main Methods:

  • Combines sparse regression with a Gaussian graphical model.
  • Infers the network structure among predictors.
  • Utilizes a Bayesian framework for variable selection.

Main Results:

  • The proposed method outperforms existing approaches in simulations.
  • Successfully identifies network-structured predictors.
  • Demonstrates efficacy in a glioblastoma survival analysis.

Conclusions:

  • The Bayesian network-guided approach is effective for variable selection in genomic data.
  • Enables discovery of biologically relevant pathways.
  • Provides a robust alternative to methods requiring a priori network information.