Ontology integration to identify protein complex in protein interaction networks.
Ontology integration to identify protein complex in protein interaction networks.
复制标题
本体集成识别蛋白质相互作用网络中的蛋白质复合物
DOI：
10.1186/1477-5956-9-s1-s7
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发表时间：
2011-10-14
期刊：
影响因子：
2
通讯作者：
Yang Z
中科院分区：
文献类型：
--
作者：
Xu B;Lin H;Yang Z
背景蛋白质复合物可以从实验数据集衍生的蛋白质相互作用网络中识别。然而，由于存在不可靠的交互和复杂的网络连接，这些分析具有挑战性。蛋白质-蛋白质相互作用与其他来源的数据的整合可用于提高蛋白质复合物检测算法的有效性。方法我们开发了新的语义相似性方法，它使用基因本体（GO）注释来衡量蛋白质-蛋白质相互作用的可靠性。通过将可靠性值分配给每个相互作用作为权重，可以将蛋白质相互作用网络转换为加权图表示。遵循先前提出的从种子顶点开始扩展聚类的聚类算法 IPCA 的方法，我们提出了一种基于新的加权蛋白质-蛋白质相互作用网络的聚类算法 OIIP，用于识别蛋白质复合物。结果该算法 OIIP 应用于酿酒酵母的蛋白质相互作用网络，并识别了许多众所周知的复合物。实验结果表明，与其他竞争方法相比，OIIP 算法具有更高的 F 度量和准确性。
BackgroundProtein complexes can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of protein complexes detection algorithms.MethodsWe have developed novel semantic similarity method, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. Following the approach of that of the previously proposed clustering algorithm IPCA which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new weighted Protein-Protein interaction networks for identifying protein complexes.ResultsThe algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches.