School of Computer Science and Technology, Dalian University of Technology, Dalian, China.
Proteome Sci. 2012 Jun 21;10 Suppl 1(Suppl 1):S18. doi: 10.1186/1477-5956-10-S1-S18.
Many biological processes recognize in particular the importance of protein complexes, and various computational approaches have been developed to identify complexes from protein-protein interaction (PPI) networks. However, high false-positive rate of PPIs leads to challenging identification.
A protein semantic similarity measure is proposed in this study, based on the ontology structure of Gene Ontology (GO) terms and GO annotations to estimate the reliability of interactions in PPI networks. Interaction pairs with low GO semantic similarity are removed from the network as unreliable interactions. Then, a cluster-expanding algorithm is used to detect complexes with core-attachment structure on filtered network. Our method is applied to three different yeast PPI networks. The effectiveness of our method is examined on two benchmark complex datasets. Experimental results show that our method performed better than other state-of-the-art approaches in most evaluation metrics.
The method detects protein complexes from large scale PPI networks by filtering GO semantic similarity. Removing interactions with low GO similarity significantly improves the performance of complex identification. The expanding strategy is also effective to identify attachment proteins of complexes.
许多生物过程特别重视蛋白质复合物的重要性，并且已经开发了各种计算方法来从蛋白质-蛋白质相互作用（PPI）网络中识别复合物。然而，高假阳性率的 PPI 导致了识别的挑战性。
本研究提出了一种基于基因本体论（GO）术语的本体结构和 GO 注释的蛋白质语义相似性度量，用于估计 PPI 网络中相互作用的可靠性。将网络中具有低 GO 语义相似性的相互作用对作为不可靠的相互作用进行删除。然后，使用聚类扩展算法在过滤后的网络上检测具有核心附着结构的复合物。我们的方法应用于三种不同的酵母 PPI 网络。在两个基准复合物数据集上对我们的方法的有效性进行了检验。实验结果表明，在大多数评估指标上，我们的方法比其他最先进的方法表现更好。
该方法通过过滤 GO 语义相似性从大规模 PPI 网络中检测蛋白质复合物。删除具有低 GO 相似性的相互作用显著提高了复合物识别的性能。扩展策略也有效地识别了复合物的附着蛋白。
