Bayesian ranking of biochemical system models

Vyshemirsky, V. and Girolami, M. A. (2008) Bayesian ranking of biochemical system models. Bioinformatics, 24(6), pp. 833-839. (doi: 10.1093/bioinformatics/btm607)

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<p><b>Motivation:</b> There often are many alternative models of a biochemical system. Distinguishing models and finding the most suitable ones is an important challenge in Systems Biology, as such model ranking, by experimental evidence, will help to judge the support of the working hypotheses forming each model.</p> <p>Bayes factors are employed as a measure of evidential preference for one model over another. Marginal likelihood is a key component of Bayes factors, however computing the marginal likelihood is a difficult problem, as it involves integration of nonlinear functions in multidimensional space. There are a number of methods available to compute the marginal likelihood approximately. A detailed investigation of such methods is required to find ones that perform appropriately for biochemical modelling.</p> <p><b>Results:</b> We assess four methods for estimation of the marginal likelihoods required for computing Bayes factors. The Prior Arithmetic Mean estimator, the Posterior Harmonic Mean estimator, the Annealed Importance Sampling and the Annealing-Melting Integration methods are investigated and compared on a typical case study in Systems Biology. This allows us to understand the stability of the analysis results and make reliable judgements in uncertain context. We investigate the variance of Bayes factor estimates, and highlight the stability of the Annealed Importance Sampling and the Annealing-Melting Integration methods for the purposes of comparing nonlinear models.</p>

Item Type:Articles
Glasgow Author(s) Enlighten ID:Vyshemirsky, Dr Vladislav and Girolami, Prof Mark
Authors: Vyshemirsky, V., and Girolami, M. A.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Bioinformatics
Publisher:Oxford University Press
ISSN (Online):1460-2059
Published Online:05 December 2007
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
396841Probabilistic Reconstruction of Signalling Pathways & Identification of Novel Transcription Factors Employing Heterogeneous Genome-Wide dataMark GirolamiMedical Research Council (MRC)G0401466/72249Computing Science