False positive reduction in protein-protein interaction predictions using gene ontology annotations

Background: Many crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated. Results: Gene Ontology (GO) annotations were used to reduce false positive protein-protein interactions (PPI) pairs resulting from computational predictions. Using experimentally obtained PPI pairs as a training dataset, eight top-ranking keywords were extracted from GO molecular function annotations. The sensitivity o...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Mahmood A. Mahdavi, Yen-Han Lin
• Keywords: BMCBI 2007, False Positive, Protein-protein Interaction Information, Protein-protein Interactions