Statistical Model Selection Methods Applied to Biological Networks
Statistical Model Selection Methods Applied to Biological Networks
复制标题
应用于生物网络的统计模型选择方法
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
C. Wiuf
中科院分区：
文献类型：
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作者：
M. Stumpf;P. Ingram;Ian Nouvel;C. Wiuf
许多生物网络被标记为无标度，因为它们的度分布可以用幂律分布近似地描述。虽然程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关于塑造网络的潜在进化过程的重要线索。一般来说，确定度分布的适当函数形式已经以一种特别的方式拟合。
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.