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A hypothesis test for equality of bayesian network models.
MedLine Citation:
PMID:  20981254     Owner:  NLM     Status:  PubMed-not-MEDLINE    
Abstract/OtherAbstract:
Bayesian network models are commonly used to model gene expression data. Some applications require a comparison of the network structure of a set of genes between varying phenotypes. In principle, separately fit models can be directly compared, but it is difficult to assign statistical significance to any observed differences. There would therefore be an advantage to the development of a rigorous hypothesis test for homogeneity of network structure. In this paper, a generalized likelihood ratio test based on Bayesian network models is developed, with significance level estimated using permutation replications. In order to be computationally feasible, a number of algorithms are introduced. First, a method for approximating multivariate distributions due to Chow and Liu (1968) is adapted, permitting the polynomial-time calculation of a maximum likelihood Bayesian network with maximum indegree of one. Second, sequential testing principles are applied to the permutation test, allowing significant reduction of computation time while preserving reported error rates used in multiple testing. The method is applied to gene-set analysis, using two sets of experimental data, and some advantage to a pathway modelling approach to this problem is reported.
Authors:
Anthony Almudevar
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Publication Detail:
Type:  Journal Article     Date:  2010-10-12
Journal Detail:
Title:  EURASIP journal on bioinformatics & systems biology     Volume:  2010     ISSN:  1687-4153     ISO Abbreviation:  EURASIP J Bioinform Syst Biol     Publication Date:  2010  
Date Detail:
Created Date:  2010-10-28     Completed Date:  2011-07-14     Revised Date:  2012-04-26    
Medline Journal Info:
Nlm Unique ID:  101263720     Medline TA:  EURASIP J Bioinform Syst Biol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  947564     Citation Subset:  -    
Affiliation:
Department of Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, NY 14642, USA.
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