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][5][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][8][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.
Research in proteomics involves studying the structure, expression, localization, interactions, and cellular roles of all proteins within a particular organism or subcomponent thereof. Researchers coined the term “proteomics” in the early 1990s to describe a new approach to studying proteins, focusing on high‐throughput analyses and on breadth rather than depth. Proteomics researchers aim to use experimental techniques that trade accuracy for volume in order to build up a complete picture of the function of large groups of proteins. Key research in the field focuses on the development of new high‐throughput techniques and the computational machinery needed to analyze the data those techniques produce.
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