Lookup NU author(s): Professor Darren Wilkinson
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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. © The Author 2007. Published by Oxford University Press.
Author(s): Wilkinson DJ
Publication type: Review
Publication status: Published
Journal: Briefings in Bioinformatics
Print publication date: 01/03/2007
ISSN (print): 1467-5463
ISSN (electronic): 1477-4054
PubMed id: 17430978