Predicting False Positives of Protein-Protein Interaction Data by Semantic Similarity Measures§
Recent technical advances in identifying protein-protein interactions (PPIs) have generated the genomic-wide interaction data, collectively collectively referred to as the interactome. These interaction data give an insight into the underlying mechanisms of biological processes. However, the PPI data determined by experimental and computational methods include an extremely large number of false positives which are not confirmed to occur in vivo. Filtering PPI data is thus a critical preprocessing step to improve analysis accuracy. Integrating Gene Ontology (GO) data is proposed in this article to assess reliability of the PPIs. We evaluate the performance of various semantic similarity measures in terms of functional consistency. Protein pairs with high semantic similarity are considered highly likely to share common functions, and therefore, are more likely to interact. We also propose a combined method of semantic similarity to apply to predicting false positive PPIs. The experimental results show that the combined hybrid method has better performance than the individual semantic similarity classifiers. The proposed classifier predicted that 58.6% of the S. cerevisiae PPIs from the BioGRID database are false positives.
No Supplementary Data
Document Type: Research Article
Publication date: 2013-07-01
More about this publication?
- Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews written by leaders in the field, covering a wide range of the integration of biology with computer and information science.
The journal focuses on reviews on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.
Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics.