Ontology integration to identify protein complex in protein interaction networks

Proteome Science

Proceedings

Bo Xu¹  Zhihao Yang¹  Hongfei Lin¹ 

[1] Department of Computer Science and Engineering, Dalian University of Technology, Dalian, China;

关键词: Gene Ontology;    Positive Predictive Value;    Protein Interaction Network;    Annotate Protein;    Vertex Weight;   

DOI  :  10.1186/1477-5956-9-S1-S7

来源: Springer

【 摘 要 】

BackgroundProtein complexes can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of protein complexes detection algorithms.MethodsWe have developed novel semantic similarity method, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. Following the approach of that of the previously proposed clustering algorithm IPCA which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new weighted Protein-Protein interaction networks for identifying protein complexes.ResultsThe algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches.

【 授权许可】

CC BY   
© Xu et al; licensee BioMed Central Ltd. 2011