Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Constructing Gene Networks Using Variational Bayesian Variable Selection

Constructing Gene Networks Using Variational Bayesian Variable Selection We propose a Bayesian approach for constructing gene networks based on microarray data. Especially, we focus on Bayesian methods that can provide soft (probabilistic) information. This soft information is attractive not only for its ability to measure the level of confidence of the solution, but also because it can be used to realize Bayesian data integration, an extremely important task in gene network research. We propose a variable selection formulation of gene regulation and develop an inference solution based on a variational Bayesian expectation maximization (VBEM) learning rule. This solution has better performance and lower complexity than the popular Monte Carlo sampling techniques. In addition, we develop a method to incorporate the often needed constraints into the VBEM algorithm, making it much more suitable for common cases of small data size. To further illustrate the advantage of the VBEM algorithm, we demonstrate a Bayesian data integration scheme using the soft information obtained from the VBEM algorithm. The efficacy of the proposed VBEM algorithm and the corresponding Bayesian data integration scheme is evaluated on both simulated data and the yeast cell cycle microarray data sets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Life MIT Press

Constructing Gene Networks Using Variational Bayesian Variable Selection

Loading next page...
 
/lp/mit-press/constructing-gene-networks-using-variational-bayesian-variable-Js053MCWuV

References (16)

Publisher
MIT Press
Copyright
© 2008 Massachusetts Institute of Technology
Subject
Articles
ISSN
1064-5462
eISSN
1530-9185
DOI
10.1162/artl.2008.14.1.65
pmid
18171131
Publisher site
See Article on Publisher Site

Abstract

We propose a Bayesian approach for constructing gene networks based on microarray data. Especially, we focus on Bayesian methods that can provide soft (probabilistic) information. This soft information is attractive not only for its ability to measure the level of confidence of the solution, but also because it can be used to realize Bayesian data integration, an extremely important task in gene network research. We propose a variable selection formulation of gene regulation and develop an inference solution based on a variational Bayesian expectation maximization (VBEM) learning rule. This solution has better performance and lower complexity than the popular Monte Carlo sampling techniques. In addition, we develop a method to incorporate the often needed constraints into the VBEM algorithm, making it much more suitable for common cases of small data size. To further illustrate the advantage of the VBEM algorithm, we demonstrate a Bayesian data integration scheme using the soft information obtained from the VBEM algorithm. The efficacy of the proposed VBEM algorithm and the corresponding Bayesian data integration scheme is evaluated on both simulated data and the yeast cell cycle microarray data sets.

Journal

Artificial LifeMIT Press

Published: Jan 1, 2008

Keywords: Gene networks; microarray data; Bayesian inference; variational Bayesian expectation maximization; Bayesian data integration

There are no references for this article.