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摘要翻译：
许多生物网络被称为无标度网络，因为它们的度分布可以近似地用幂律分布来描述。虽然度分布并不能概括网络的所有方面，但人们经常认为，它的功能形式包含了塑造网络的潜在进化过程的重要线索。通常，确定学位分布的适当功能形式是以一种特殊的方式进行的。在这里，我们应用形式统计模型选择方法来确定哪种功能形式最好地描述蛋白质相互作用和代谢网络的程度分布。我们将度分布解释为一类概率模型，并利用最大似然推理、复合似然方法、Akaike信息准则和拟合优度检验确定哪种模型对经验数据提供了最好的描述。整个数据被用来确定在给定模型（例如，无标度图或随机图）下最好解释数据的参数。正如我们将要展示的，目前来自不同生物体的蛋白质相互作用和代谢网络数据表明，简单的无标度模型不能提供对真实网络数据的充分描述。
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英文标题：
《Statistical model selection methods applied to biological networks》
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作者：
M.P.H. Stumpf, P.J. Ingram, I. Nouvel, C. Wiuf
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最新提交年份：
2005
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分类信息：
一级分类：Quantitative Biology 数量生物学
二级分类：Molecular Networks 分子网络
分类描述：Gene regulation, signal transduction, proteomics, metabolomics, gene and enzymatic networks
基因调控、信号转导、蛋白质组学、代谢组学、基因和酶网络
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一级分类：Quantitative Biology 数量生物学
二级分类：Other Quantitative Biology 其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
Many biological networks have been labelled scale-free as their degree distribution can be approximately described by a powerlaw distribution. While the degree distribution does not summarize all aspects of a network it has often been suggested that its functional form contains important clues as to underlying evolutionary processes that have shaped the network. Generally determining the appropriate functional form for the degree distribution has been fitted in an ad-hoc fashion. Here we apply formal statistical model selection methods to determine which functional form best describes degree distributions of protein interaction and metabolic networks. We interpret the degree distribution as belonging to a class of probability models and determine which of these models provides the best description for the empirical data using maximum likelihood inference, composite likelihood methods, the Akaike information criterion and goodness-of-fit tests. The whole data is used in order to determine the parameter that best explains the data under a given model (e.g. scale-free or random graph). As we will show, present protein interaction and metabolic network data from different organisms suggests that simple scale-free models do not provide an adequate description of real network data.
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PDF链接：
https://arxiv.org/pdf/q-bio/0506013
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