Protein Complex Identification by Integrating Protein-Protein Interaction Evidence from Multiple Sources |
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| Authors: | Bo Xu Hongfei Lin Yang Chen Zhihao Yang Hongfang Liu |
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| Affiliation: | 1. School of Computer Science and Technology, Dalian University of Technology, Dalian, China.; 2. Department of Health Science Research, Mayo Clinic, Rochester, Minnesota, United States of America.; 3. Department of Computer Science, Virginia Tech, Falls Church, Virginia, United States of America.; MRC National Institute for Medical Research, United Kingdom, |
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| Abstract: | BackgroundUnderstanding protein complexes is important for understanding the science of cellular organization and function. Many computational methods have been developed to identify protein complexes from experimentally obtained protein-protein interaction (PPI) networks. However, interaction information obtained experimentally can be unreliable and incomplete. Reconstructing these PPI networks with PPI evidences from other sources can improve protein complex identification.ResultsWe combined PPI information from 6 different sources and obtained a reconstructed PPI network for yeast through machine learning. Some popular protein complex identification methods were then applied to detect yeast protein complexes using the new PPI networks. Our evaluation indicates that protein complex identification algorithms using the reconstructed PPI network significantly outperform ones on experimentally verified PPI networks.ConclusionsWe conclude that incorporating PPI information from other sources can improve the effectiveness of protein complex identification. |
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