Bayesian ranking of biochemical system models
Bayesian ranking of biochemical system models
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DOI：
10.1093/bioinformatics/btm607
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发表时间：
2008-03-01
期刊：
影响因子：
5.8
通讯作者：
Girolami, Mark A.
中科院分区：
文献类型：
--
作者：
Vyshemirsky, Vladislav;Girolami, Mark A.
动机：生化系统通常有许多不同的模型。区分模型并找到最合适的模型是系统生物学中的一个重要挑战，因为根据实验证据对模型进行排序将有助于判断构成每个模型的工作假设的支持程度。边际似然是贝叶斯因子的一个重要组成部分，然而计算边际似然是一个困难的问题，因为它涉及到多维空间中的非线性函数的积分。有许多方法可以用来近似计算边际似然。需要对这些方法进行详细的调查，以找到适合于生化建模的方法。结果：我们评估了四种估计计算贝叶斯因子所需的边际概率的方法。在系统生物学的典型案例研究中，对先验算术平均估计量、后验调和均值估计量、退火重要抽样和退火-熔融积分方法进行了研究和比较。这使我们能够了解分析结果的稳定性，并在不确定的情况下做出可靠的判断。为了比较非线性模型，我们研究了贝叶斯因子估计的方差，并强调了退火重要抽样和退火-熔融积分方法的稳定性。
Motivation: There often are many alternative models of a biochemical system. Distinguishing models and finding the most suitable ones is an important challenge in Systems Biology, as such model ranking, by experimental evidence, will help to judge the support of the working hypotheses forming each model.Bayes factors are employed as a measure of evidential preference for one model over another. Marginal likelihood is a key component of Bayes factors, however computing the marginal likelihood is a difficult problem, as it involves integration of nonlinear functions in multidimensional space. There are a number of methods available to compute the marginal likelihood approximately. A detailed investigation of such methods is required to find ones that perform appropriately for biochemical modelling.Results: We assess four methods for estimation of the marginal likelihoods required for computing Bayes factors. The Prior Arithmetic Mean estimator, the Posterior Harmonic Mean estimator, the Annealed Importance Sampling and the Annealing-Melting Integration methods are investigated and compared on a typical case study in Systems Biology. This allows us to understand the stability of the analysis results and make reliable judgements in uncertain context. We investigate the variance of Bayes factor estimates, and highlight the stability of the Annealed Importance Sampling and the Annealing-Melting Integration methods for the purposes of comparing nonlinear models.