Bayesian methods in bioinformatics and computational systems biology.

by: Darren J J Wilkinson

Brief Bioinform (12 April 2007)

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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.

@article{citeulike:1226851, 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.}, author = {Wilkinson, Darren J J. }, citeulike-article-id = {1226851}, doi = {10.1093/bib/bbm007}, issn = {1467-5463}, journal = {Brief Bioinform}, keywords = {bayesian, bioinformatics}, month = {April}, posted-at = {2007-04-24 15:48:13}, priority = {2}, title = {Bayesian methods in bioinformatics and computational systems biology.}, url = {http://dx.doi.org/10.1093/bib/bbm007}, year = {2007} }

RIS record

TY - JOUR ID - citeulike:1226851 L3 - citeulike-article-id:1226851 TI - Bayesian methods in bioinformatics and computational systems biology. JF - Brief Bioinform SN - 1467-5463 N2 - 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. KW - bayesian KW - bioinformatics AU - Wilkinson, Darren J PY - 2007/04/12/ UR - http://dx.doi.org/10.1093/bib/bbm007 ER -

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