Protein complexes play a key role in many biological processes. Various computational approaches have been developed to identify complexes from protein-protein interaction (PPI) networks. However, high false-positive rate of PPIs makes the identification challenging. In this paper, we propose a protein semantic similarity measure 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 applied to identify complexes with core-attachment structure on the filtered network. We have applied our method on three different yeast PPI networks. The effectiveness of our method is examined on two benchmark complex datasets. Experimental results show that our method outperforms other state-of-the-art approaches in most evaluation metrics. Removing interactions with low similarity significantly improves the performance of complex identification.
['Jian Wang', 'Dong Xie', 'Hongfei Lin', 'Zhihao Yang', 'Yijia Zhang']
Identifying Protein Complexes from PPI Networks Using GO Semantic Similarity