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