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Bayesian methods in bioinformatics and computational systems biology

Lookup NU author(s): Professor Darren Wilkinson

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Abstract

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.

Publication metadata

Author(s): Wilkinson DJ

Publication type: Review

Publication status: Published

Journal: Briefings in Bioinformatics

Year: 2007

Volume: 8

Issue: 2

Pages: 109-116

Print publication date: 01/03/2007

ISSN (print): 1467-5463

ISSN (electronic): 1477-4054

URL: http://dx.doi.org/10.1093/bib/bbm007

DOI: 10.1093/bib/bbm007

PubMed id: 17430978

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