Abstract
Recent computational analyses of protein interaction networks have attempted to understand cellular organizations, processes and functions. However, they have encountered difficulties due to unreliable interaction data and the complexity of the networks. In this paper, we propose the integration of protein interaction networks with gene ontology annotations for assessing the reliability of current protein-protein interaction data. The interaction reliability can be used for building weighted protein interaction networks. We apply an information flow-based modularization algorithm to the weighted protein interaction networks. Our experimental results show that the interaction reliability between two proteins is positively correlated to the likelihood of functional and locational associations. We finally demonstrate that our approach identifies accurate modules in the protein interaction networks with high statistical confidence with respect to biological function and cellular localization. Moreover, this algorithm outperforms our previous method (Cho et al., 2006) integrating with genetic co-expressional profiles
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