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Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics$
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Christine Sinoquet and Raphaël Mourad

Print publication date: 2014

Print ISBN-13: 9780198709022

Published to Oxford Scholarship Online: December 2014

DOI: 10.1093/acprof:oso/9780198709022.001.0001

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Comparison of Mixture Bayesian and Mixture Regression Approaches to Infer Gene Networks

Comparison of Mixture Bayesian and Mixture Regression Approaches to Infer Gene Networks

Chapter:
(p.105) Chapter 4 Comparison of Mixture Bayesian and Mixture Regression Approaches to Infer Gene Networks
Source:
Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics
Author(s):

Sandra L. Rodriguez–Zas

Bruce R. Southey

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198709022.003.0004

Most Bayesian network applications to gene network reconstruction assume a single distributional model across all the samples and treatments analyzed. This assumption is likely to be unrealistic especially when describing the relationship between genes across a range of treatments with potentially different impacts on the networks. To address this limitation, a mixture Bayesian network approach has been developed. Besides, the equivalence between Bayesian networks and regression approaches has been demonstrated. Here, two strategies are compared: the mixture Bayesian network approach and the mixture regression approach, when used for the purpose of gene network inference. The finite mixture model that is integrated into both strategies allows the characterization of gene relationships unique to particular conditions as well as the identification of interactions shared across conditions. The chapter reviews performances on real data describing a pathway analyzed under up to nine different experimental conditions, and highlights the strengths of the approaches evaluated.

Keywords:   gene network, mixture model, Bayesian network

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