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Briefings in Bioinformatics Advance Access originally published online on April 12, 2007
Briefings in Bioinformatics 2007 8(2):109-116; doi:10.1093/bib/bbm007
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© The Author 2007. Published by Oxford University Press. For Permissions, please email: journals.permissions@oxfordjournals.org

Bayesian methods in bioinformatics and computational systems biology

Darren J. Wilkinson

Corresponding author. D.J. Wilkinson, School of Mathematics & Statistics, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK. Tel: + 44-191-2227320; E-mail: d.j.wilkinson{at}ncl.ac.uk

Bayesian methods are valuable, inter alia, whenever there is a need to extract information from data that are uncertain or subject to any kind of error or noise (including measurement error and experimental error, as well as noise or random variation intrinsic to the process of interest). Bayesian methods offer a number of advantages over more conventional statistical techniques that make them particularly appropriate for complex data. It is therefore no surprise that Bayesian methods are becoming more widely used in the fields of genetics, genomics, bioinformatics and computational systems biology, where making sense of complex noisy data is the norm. This review provides an introduction to the growing literature in this area, with particular emphasis on recent developments in Bayesian bioinformatics relevant to computational systems biology.

Keywords: Bayesian inference, computational systems biology, networks, graphical models, quantitative, predictive biology


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