Shui Yong, Cho Young-Rae
IEEE Trans Nanobioscience. 2016 Jun;15(4):380-389. doi: 10.1109/TNB.2016.2555802. Epub 2016 Apr 21.
Network alignment is a computational technique to identify topological similarity of graph data by mapping link patterns. In bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks to discover evolutionarily conserved substructures at the system level. In particular, local network alignment of PPI networks searches for conserved functional components between species and predicts unknown protein complexes and signaling pathways. In this article, we present a novel approach of local network alignment by semantic mapping. While most previous methods find protein matches between species by sequence homology, our approach uses semantic similarity. Given Gene Ontology (GO) and its annotation data, we estimate functional closeness between two proteins by measuring their semantic similarity. We adopted a new semantic similarity measure, simVICD, which has the best performance for PPI validation and functional match. We tested alignment between the PPI networks of well-studied yeast protein complexes and the genome-wide PPI network of human in order to predict human protein complexes. The experimental results demonstrate that our approach has higher accuracy in protein complex prediction than graph clustering algorithms, and higher efficiency than previous network alignment algorithms.
网络比对是一种通过映射链接模式来识别图数据拓扑相似性的计算技术。在生物信息学中，网络比对算法已应用于蛋白质-蛋白质相互作用（PPI）网络，以在系统层面发现进化上保守的子结构。特别是，PPI网络的局部网络比对可在物种间寻找保守的功能组件，并预测未知的蛋白质复合物和信号通路。在本文中，我们提出了一种通过语义映射进行局部网络比对的新方法。虽然大多数先前的方法通过序列同源性来寻找物种间的蛋白质匹配，但我们的方法使用语义相似性。给定基因本体（GO）及其注释数据，我们通过测量两个蛋白质之间的语义相似性来估计它们的功能接近度。我们采用了一种新的语义相似性度量方法simVICD，它在PPI验证和功能匹配方面具有最佳性能。我们测试了经过充分研究的酵母蛋白质复合物的PPI网络与人类全基因组PPI网络之间的比对，以预测人类蛋白质复合物。实验结果表明，我们的方法在蛋白质复合物预测方面比图聚类算法具有更高的准确性，并且比先前的网络比对算法具有更高的效率。
