Alterovitz Gil, Liu Jonathan, Afkhami Ehsan, Ramoni Marco F
Division of Health Sciences and Technology, Harvard University and Massachusetts Institute of Technology, Boston, MA, USA.
Proteomics. 2007 Aug;7(16):2843-55. doi: 10.1002/pmic.200700422.
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.
在过去几年中，生物和医学数据呈指数级增长[1,2]。特别是，蛋白质组学领域见证了自动化极大地改变了数据生成的速度[3]。系统地整合先验信息的分析对于从大量复杂数据中进行推断变得至关重要[4-6]。贝叶斯方法可以帮助捕捉此类信息，并通过严格的概率框架将其无缝整合。本文首先回顾贝叶斯方法背后的背景数学：从参数估计到贝叶斯网络。接着文章讨论了新兴的贝叶斯方法如何已经成功应用于蛋白质组学的研究，蛋白质组学是一个贝叶斯方法特别适用的领域[7-9]。在回顾了生物背景下贝叶斯方法主题的文献后，文章讨论了蛋白质组学领域的一些最新应用和新兴方向。
