Chapter

Bayesian Models for Variable Selection that Incorporate Biological Information*

Marina Vannucci and Francesco C. Stingo

in Bayesian Statistics 9

Published in print October 2011 | ISBN: 9780199694587
Published online January 2012 | e-ISBN: 9780191731921 | DOI: http://dx.doi.org/10.1093/acprof:oso/9780199694587.003.0022
Bayesian Models for Variable Selection that Incorporate Biological Information*

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Variable selection has been the focus of much research in recent years. Bayesian methods have found many successful applications, particularly in situations where the amount of measured variables can be much greater than the number of observations. One such example is the analysis of genomics data. In this paper we first review Bayesian variable selection methods for linear settings, including regression and classification models. We focus in particular on recent prior constructions that have been used for the analysis of genomic data and briefly describe two novel applications that integrate different sources of biological information into the analysis of experimental data. Next, we address variable selection for a different modeling context, i.e., mixture models. We address both clustering and discriminant analysis settings and conclude with an application to gene expression data for patients affected by leukemia.

Keywords: Classification and Clustering; Discriminant Analysis; Gene Networks; Markov Random Field Priors; Pathways; Regression Models; Variable Selection

Chapter.  11291 words.  Illustrated.

Subjects: Probability and Statistics

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