title: Bayesian methods for proteomics.
abstract: 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.
authors.affiliation: Division of Health Sciences and Technology, Harvard University and Massachusetts Institute of Technology, Boston, MA, USA. gil@mit.edu
authors.forename: Gil
authors.initials: G
authors.lastname: Alterovitz
authors.forename: Jonathan
authors.initials: J
authors.lastname: Liu
authors.forename: Ehsan
authors.initials: E
authors.lastname: Afkhami
authors.forename: Marco F
authors.initials: MF
authors.lastname: Ramoni
mesh_terms: D001499:Bayes Theorem; D008958:Models, Molecular; D010455:Peptides / Q000737:chemistry; D010802:Phylogeny; D040901:Proteomics; D015398:Signal Transduction
chemical_list: D010455:Peptides