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Yang L, Tang X. Protein-protein interactions prediction based on iterative clique extension with gene ontology filtering. ScientificWorldJournal. 2014;2014:523634doi: 10.1155/2014/523634.
Yang, L., & Tang, X. (2014). Protein-protein interactions prediction based on iterative clique extension with gene ontology filtering. TheScientificWorldJournal, 2014523634. https://doi.org/10.1155/2014/523634
Yang, Lei, and Tang, Xianglong. "Protein-protein interactions prediction based on iterative clique extension with gene ontology filtering." TheScientificWorldJournal vol. 2014 (2014): 523634. doi: https://doi.org/10.1155/2014/523634
Yang L, Tang X. Protein-protein interactions prediction based on iterative clique extension with gene ontology filtering. ScientificWorldJournal. 2014 Jan 22;2014:523634. doi: 10.1155/2014/523634. eCollection 2014. PMID: 24578640; PMCID: PMC3919085.
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ScientificWorldJournal. 2014 Jan 22;2014:523634. doi: 10.1155/2014/523634. eCollection 2014.

Protein-protein interactions prediction based on iterative clique extension with gene ontology filtering.

TheScientificWorldJournal

Lei Yang, Xianglong Tang

Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Mailbox 352, 92 West Dazhi Street, Nan Gang District, Harbin 150001, China.

PMID: 24578640 PMCID: PMC3919085 DOI: 10.1155/2014/523634

Abstract

Cliques (maximal complete subnets) in protein-protein interaction (PPI) network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO) annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP) and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.

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