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Open Access Highly Accessed Methodology article

Semantic integration to identify overlapping functional modules in protein interaction networks

Young-Rae Cho1*, Woochang Hwang1, Murali Ramanathan2 and Aidong Zhang1

Author Affiliations

1 Department of Computer Science and Engineering, State University of New York, Buffalo, NY, USA

2 Department of Pharmaceutical Science, State University of New York, Buffalo, NY, USA

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BMC Bioinformatics 2007, 8:265  doi:10.1186/1471-2105-8-265

Published: 24 July 2007

Abstract

Background

The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules 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 functional module detection algorithms.

Results

We have developed novel metrics, called semantic similarity and semantic interactivity, 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. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches.

Conclusion

The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.