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arXiv 0907.3156q-bio.QMq-bio.BM
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生物物理时间序列的学习率和状态:模型选择和单分子FRET数据的贝叶斯方法

Learning Rates and States from Biophysical Time Series: A Bayesian Approach to Model Selection and Single-Molecule FRET Data

Jonathan E. Bronson, Jingyi Fei, Jake M. Hofman, Ruben L. Gonzalez,, Chris H. Wiggins

摘要:

由单分子福斯特共振能量转移(sm-FRET)实验提供的时间序列数据提供了推断的机会,不仅模型参数描述的分子复合物,如速率常数,但也关于模型本身的信息,如构象状态的数量。解决是否或有多少这样的状态存在需要一个仔细的方法来选择模型的问题,这里的意思是区分不同数量的状态模型。模型选择最直接的方法是将最大似然的一般思想(选择最可能的参数值)推广到最大证据:选择最可能的模型。在这两种情况下,这样的推理提出了一个巨大的计算挑战,我们在这里解决利用近似技术称为变分贝叶斯。我们演示了如何将这种技术可以应用到时间数据,如smFRET时间序列;显示优越的统计一致性相对于最大似然法;并说明如何在这种概率或生成建模模型选择可以促进分析密切相关的时间数据,目前流行的生物物理学。本分析中使用的源代码(包括图形用户界面)可通过http://vbFRET.sourceforge.net获得

英文摘要:

Time series data provided by single-molecule Forster resonance energy transfer (sm-FRET) experiments offer the opportunity to infer not only model parameters describing molecular complexes, e.g. rate constants, but also information about the model itself, e.g. the number of conformational states. Resolving whether or how many of such states exist requires a careful approach to the problem of model selection, here meaning discriminating among models with differing numbers of states. The most straightforward approach to model selection generalizes the common idea of maximum likelihood-selecting the most likely parameter values-to maximum evidence: selecting the most likely model. In either case, such inference presents a tremendous computational challenge, which we here address by exploiting an approximation technique termed variational Bayes. We demonstrate how this technique can be applied to temporal data such as smFRET time series; show superior statistical consistency relative to the maximum likelihood approach; and illustrate how model selection in such probabilistic or generative modeling can facilitate analysis of closely related temporal data currently prevalent in biophysics. Source code used in this analysis, including a graphical user interface, is available open source via http://vbFRET.sourceforge.net

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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