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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >A Coclustering Approach for Mining Large Protein-Protein Interaction Networks
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A Coclustering Approach for Mining Large Protein-Protein Interaction Networks

机译：大型蛋白质-蛋白质相互作用网络的共聚方法

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摘要

Several approaches have been presented in the literature to cluster Protein-Protein Interaction (PPI) networks. They can be grouped in two main categories: those allowing a protein to participate in different clusters and those generating only nonoverlapping clusters. In both cases, a challenging task is to find a suitable compromise between the biological relevance of the results and a comprehensive coverage of the analyzed networks. Indeed, methods returning high accurate results are often able to cover only small parts of the input PPI network, especially when low-characterized networks are considered. We present a coclustering-based technique able to generate both overlapping and nonoverlapping clusters. The density of the clusters to search for can also be set by the user. We tested our method on the two networks of yeast and human, and compared it to other five well-known techniques on the same interaction data sets. The results showed that, for all the examples considered, our approach always reaches a good compromise between accuracy and network coverage. Furthermore, the behavior of our algorithm is not influenced by the structure of the input network, different from all the techniques considered in the comparison, which returned very good results on the yeast network, while on the human network their outcomes are rather poor.
机译：文献中已经提出了几种方法来聚类蛋白质-蛋白质相互作用（PPI）网络。它们可以分为两个主要类别：允许蛋白质参与不同簇的蛋白和仅产生不重叠簇的蛋白。在这两种情况下，一项艰巨的任务是在结果的生物学相关性与分析网络的全面覆盖范围之间找到合适的折衷方案。实际上，返回高精度结果的方法通常只能覆盖输入PPI网络的一小部分，尤其是在考虑了低特征网络时。我们提出了一种基于共聚簇的技术，能够生成重叠和不重叠的簇。用户也可以设置要搜索的簇的密度。我们在酵母和人的两个网络上测试了我们的方法，并在相同的交互数据集上将其与其他五种知名技术进行了比较。结果表明，对于所考虑的所有示例，我们的方法始终在准确性和网络覆盖范围之间达成良好的折衷。此外，与比较中考虑的所有技术不同，我们算法的行为不受输入网络结构的影响，后者在酵母网络上返回了非常好的结果，而在人际网络上，它们的结果却很差。

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