A new two-stage method for revealing missing parts of edges in protein-protein interaction networks

• Published in: PloS one 2017-05, Vol.12 (5), p.e0177029-e0177029
• Main Authors: Zhang, Wei, Xu, Jia, Li, Yuanyuan, Zou, Xiufen
• Format: Article
• Language: English
• Subjects: Affinity; Algorithms; Augmented reality; Bioinformatics; Biological computing; Biological properties; Biology and Life Sciences; Computation; Computer and Information Sciences; Computer applications; Consortia; Construction; Correlation coefficient; Data mining; Data processing; Engineering; Eukaryotes; Gene expression; Gene Expression Regulation, Fungal; Genes; Genetics; Genomes; Genomics - methods; Homology; Identification methods; Information dissemination; Knowledge representation; Links; Methods; Modules; Nodes; Nucleic acids; Ontology; Phylogeny; Physical Sciences; Physiological aspects; Predictions; Probability theory; Prokaryotes; Protein interaction; Protein Interaction Mapping - methods; Protein Interaction Maps; Protein purification; Protein structure; Protein-protein interactions; Proteins; Research and Analysis Methods; Saccharomyces cerevisiae; Saccharomyces cerevisiae - genetics; Saccharomyces cerevisiae - metabolism; Saccharomyces cerevisiae Proteins - genetics; Saccharomyces cerevisiae Proteins - metabolism; Semantics; Social interactions; Social organization; Statistical analysis; Technology; Topology
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0177029

With the increasing availability of high-throughput data, various computational methods have recently been developed for understanding the cell through protein-protein interaction (PPI) networks at a systems level. However, due to the incompleteness of the original PPI networks those efforts have been significantly hindered. In this paper, we propose a two stage method to predict underlying links between two originally unlinked protein pairs. First, we measure gene expression and gene functional similarly between unlinked protein pairs on Saccharomyces cerevisiae benchmark network and obtain new constructed networks. Then, we select the significant part of the new predicted links by analyzing the difference between essential proteins that have been identified based on the new constructed networks and the original network. Furthermore, we validate the performance of the new method by using the reliable and comprehensive PPI dataset obtained from the STRING database and compare the new proposed method with four other random walk-based methods. Comparing the results indicates that the new proposed strategy performs well in predicting underlying links. This study provides a general paradigm for predicting new interactions between protein pairs and offers new insights into identifying essential proteins.