ppiPre: predicting protein-protein interactions by combining heterogeneous features.
ppiPre: predicting protein-protein interactions by combining heterogeneous features.
复制标题
ppiPre：通过组合异质特征预测蛋白质-蛋白质相互作用
DOI：
10.1186/1752-0509-7-s2-s8
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发表时间：
2013
影响因子：
--
通讯作者：
Wang B
中科院分区：
文献类型：
--
作者：
Deng Y;Gao L;Wang B
蛋白-蛋白相互作用（PPIs）在细胞过程中起着至关重要的作用。由于目前的生物实验技术耗时且昂贵，并且结果存在不完整和噪声问题，因此开发计算方法和软件工具来预测ppi是必要的。虽然提出了几种方法，但支持的物种通常有限，并且通常需要其他物种的同源相互作用，蛋白质序列和蛋白质表达等额外数据。不同特征对不同类型PPI数据的预测能力尚未得到研究。在本文中，我们提出了一个开源的PPI分析和预测框架ppiPre，该框架使用异构特征的组合，包括三个基于go的语义相似度，一个基于kegg的共通路相似度和三个基于拓扑的相似度。它支持多达20个物种。用户只需要原始PPI数据和金标准PPI数据。在二元和共复金标准酵母PPI数据集上的实验表明，不同特征对不同类型PPI数据集的预测能力存在较大差异。在两个数据集上的预测性能表明，ppiPre能够处理不同类型和大小的PPI数据。ppiPre是用R语言实现的，可以在CRAN （http://cran.r-project.org/web/packages/ppiPre/）上免费获得。我们将我们的框架应用于二元和共复金标准PPI数据集。通过对GO的三个方面的详细分析，可以看出不同的GO方面应该用于不同类型的数据集，将GO的三个方面结合起来往往会得到最好的结果。分析还表明，在预测共复PPI数据时，仅使用基于PPI网络拓扑的特征可以获得很好的结果。ppiPre为PPI数据分析提供了有用的功能，可用于预测多种物种的PPI。
BackgroundProtein-protein interactions (PPIs) are crucial in cellular processes. Since the current biological experimental techniques are time-consuming and expensive, and the results suffer from the problems of incompleteness and noise, developing computational methods and software tools to predict PPIs is necessary. Although several approaches have been proposed, the species supported are often limited and additional data like homologous interactions in other species, protein sequence and protein expression are often required. And predictive abilities of different features for different kinds of PPI data have not been studied.ResultsIn this paper, we propose ppiPre, an open-source framework for PPI analysis and prediction using a combination of heterogeneous features including three GO-based semantic similarities, one KEGG-based co-pathway similarity and three topology-based similarities. It supports up to twenty species. Only the original PPI data and gold-standard PPI data are required from users. The experiments on binary and co-complex gold-standard yeast PPI data sets show that there exist big differences among the predictive abilities of different features on different kinds of PPI data sets. And the prediction performance on the two data sets shows that ppiPre is capable of handling PPI data in different kinds and sizes. ppiPre is implemented in the R language and is freely available on the CRAN ( http://cran.r-project.org/web/packages/ppiPre/ ).ConclusionsWe applied our framework to both binary and co-complex gold-standard PPI data sets. The detailed analysis on three GO aspects suggests that different GO aspects should be used on different kinds of data sets, and that combining all the three aspects of GO often gets the best result. The analysis also shows that using only features based solely on the topology of the PPI network can get a very good result when predicting the co-complex PPI data. ppiPre provides useful functions for analysing PPI data and can be used to predict PPIs for multiple species.
影响因子：
12.3
作者：
van Noort, Vera;Snel, Berend;Huynen, Martijn A
通讯作者：
Huynen, Martijn A