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Bayesian methods for proteomics
Author(s) -
Alterovitz Gil,
Liu Jonathan,
Afkhami Ehsan,
Ramoni Marco F.
Publication year - 2007
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.200700422
Subject(s) - bayesian probability , computer science , data science , field (mathematics) , proteomics , bayesian statistics , variable order bayesian network , probabilistic logic , bayesian inference , machine learning , artificial intelligence , mathematics , biology , biochemistry , pure mathematics , gene
Biological and medical data have been growing exponentially over the past several years [1, 2]. In particular, proteomics has seen automation dramatically change the rate at which data are generated [3]. Analysis that systemically incorporates prior information is becoming essential to making inferences about the myriad, complex data [4–6]. A Bayesian approach can help capture such information and incorporate it seamlessly through a rigorous, probabilistic framework. This paper starts with a review of the background mathematics behind the Bayesian methodology: from parameter estimation to Bayesian networks. The article then goes on to discuss how emerging Bayesian approaches have already been successfully applied to research across proteomics, a field for which Bayesian methods are particularly well suited [7–9]. After reviewing the literature on the subject of Bayesian methods in biological contexts, the article discusses some of the recent applications in proteomics and emerging directions in the field.