Effect of dataset selection on the topological interpretation of protein interaction networks.
Effect of dataset selection on the topological interpretation of protein interaction networks.
复制标题
数据集选择对蛋白质相互作用网络的拓扑解释的影响。
DOI：
10.1186/1471-2164-6-131
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发表时间：
2005-09-20
期刊：
影响因子：
4.4
通讯作者：
Oliver SG
中科院分区：
文献类型：
--
作者：
Hakes L;Robertson DL;Oliver SG
对酵母蛋白质相互作用网络的研究揭示了网络中单个蛋白质的连接性与其邻居的平均连接性之间的明显相关性。虽然已经提出了一些生物学机制来解释这些发现，但这些研究中所包含的特定数据集的意义和影响尚未得到充分的重视。我们展示了如何使用不同的相互作用数据集，如高通量或小规模研究产生的数据集，以及用于推导成对蛋白质相互作用的不同建模方法，可以显着改变这些网络的拓扑结构。此外，我们发现，在这些网络中发现的一些先前报道的功能可能只是实验或方法错误和偏见的结果。在进行基于网络的研究时，必须定义术语“相互作用”的含义，并且在解释生成的网络拓扑结构时必须考虑到这一点。必须考虑所包括的数据类型，并且必须选择考虑到数据特性的适当控制
Studies of the yeast protein interaction network have revealed distinct correlations between the connectivity of individual proteins within the network and the average connectivity of their neighbours. Although a number of biological mechanisms have been proposed to account for these findings, the significance and influence of the specific datasets included in these studies has not been appreciated adequately. We show how the use of different interaction data sets, such as those resulting from high-throughput or small-scale studies, and different modelling methodologies for the derivation pair-wise protein interactions, can dramatically change the topology of these networks. Furthermore, we show that some of the previously reported features identified in these networks may simply be the result of experimental or methodological errors and biases. When performing network-based studies, it is essential to define what is meant by the term "interaction" and this must be taken into account when interpreting the topologies of the networks generated. Consideration must be given to the type of data included and appropriate controls that take into account the idiosyncrasies of the data must be selected
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