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Assessing Reliability of Protein-Protein Interactions by Semantic Data Integration

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3 Author(s)
Young-Rae Cho ; State Univ. of New York at Buffalo, Buffalo ; Woochang Hwang ; Aidong Zhang

The systematic analysis of protein-protein interactions is a fundamental step for understanding of cellular organization, processes and functions. Recent high-throughput experiments have produced an enormous amount of protein-protein interaction data. However, the analysis of protein-protein interactions has a limitation in effectiveness because of the unreliability of the interaction data. In this paper, we apply semantic similarity measures to quantifying the reliability of protein-protein interactions. We also propose a novel metric, which is called semantic interactivity, to measure the interaction reliability by the integration of gene ontology annotations. We evaluate the measurements by comparing the interaction reliability between proteins to their functional co-occurrence. The results show that the interaction reliability measured by semantic interactivity has a positive correlation with the functional association between the interacting proteins. Finally, we demonstrate that the semantic interactivity measure can accurately detect potential false positive interactions.

Published in:

Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007)

Date of Conference:

28-31 Oct. 2007