Abstract
Recent developments in experiments have resulted in the publication of many high-quality, large-scale protein-protein interaction (PPI) data. Unfortunately, a significant proportion of PPI networks have been found to contain false positives, which have negative effects on the further research of PPI networks. We construct an uncertain protein-protein interaction (UPPI) network, in which each protein-protein interaction is assigned with an existence probability using the topology of the PPI network solely. Based on the uncertainty theory, we propose the concept of expected density to assess the density degree of a subgraph, the concept of the relative degree to describe the relationship between a protein and a subgraph in a UPPI network. To verify the effectiveness of the UPPI network, we propose a novel complex prediction method named CPUT (Complex Prediction based on Uncertainty Theory). In CPUT, the expected density combined with the absolute degree is used to determine whether a mined subgraph from the UPPI network can be represented as a core component with high cohesion and low coupling while the relative degree is the criterion of binding an attachment protein to a core component to form a complex. We employ CPUT and the existing competitive algorithms on two yeast PPI networks. Experimental results indicate that CPUT performs significantly better than the state-of-the-art methods.
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References
Ito, T., et al.: A comprehensive two-hybrid analysis to explore the yeast protein interactome. PNAS 98, 4569–4574 (2001)
Rigaut, G., et al.: A generic protein purification method for protein complex characterization and proteome exploration. Nature Biotechnology 17, 1030–1032 (1999)
Ho, Y., et al.: Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 405, 180–183 (2002)
Mrowka, R., Patzak, A., Herzel, H.: Is There a Bias in Proteome Research? Genome Research 11, 1971–1973 (2001)
Mering, C.V., et al.: Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417, 399–403 (2002)
Tsoka, S., Ouzounis, C.A.: Prediction of protein interactions: metabolic enzymes are frequently involved in gene fusion. Nature Genetics 26, 141–142 (2000)
Wojcik, J., Schächter, V.: Protein–protein interaction map inference using interacting domain profile pairs. Bioinformatics 17, 296–305 (2001)
Yamada, T., Kanehisa, M., Goto, S.: Extraction of phylogenetic network modules from the metabolic network. BMC Bioinformatics 7, 130 (2006)
Wu, J., Kasif, S., DeLisi, C.: Identification of functional links between genes using phylogenetic profiles. Bioinformatics 19, 1524–1530 (2003)
Albert, I., Albert, R.: Identification of functional links between genes using phylogenetic profiles. Bioinformatics 20, 3346–3352 (2004)
Bock, J.R., Gough, D.A.: Identification of functional links between genes using phylogenetic profiles. Bioinformatics 19, 125–135 (2003)
Lo, S.L., et al.: Effect of training datasets on support vector machine prediction of protein-protein interactions. Proteomics 5, 876–884 (2005)
Deane, C.M., et al.: Protein interactions: two methods for assessment of the reliability of high throughput observations. Molecular & Cellular Proteomics 1, 349–356 (2002)
D’haeseleer, P., Church, G.M.: Estimating and improving protein interaction error rates. In: Proc. IEEE Computational Systems Bioinformatics Conference, USA, pp. 216–223 (2004)
Gilchrist, M.A., et al.: A statistical framework for combining and interpreting proteomic datasets. Bioinformatics 20, 689–700 (2004)
Mering, V.C., et al.: Comparative assessment of large-scale data sets of proteinprotein interactions. Nature 417, 399–403 (2002)
Hwang, W., et al.: A novel functional module detection algorithm for protein-protein interaction networks. Algorithms for Molecular Biology 1, 24 (2006)
Ulitsky, I., Shamir, R.: Identifying functional modules using expression profiles and confidence-scored protein interactions. Bioinformatics 25, 1158–1164 (2009)
Gabow, A.P., et al.: Improving protein function prediction methods with integrated literature data. BMC Bioinformatics 9, 198 (2008)
Hu, L., et al.: Predicting Functions of Proteins in Mouse Based on Weighted Protein-Protein Interaction Network and Protein Hybrid Properties. PLoS ONE 6, e14556 (2011)
Peng, W., et al.: Iteration method for predicting essential proteins based on orthology and protein-protein interaction networks. BMC Systems Biology 6, 87 (2012)
Bader, G.D., Hogue, C.W.: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4, 2 (2003)
Enright, A.J., Dongen, S.V., Ouzounis, C.A.: An efficient algorithm for large-scale detection of protein families. Nucleic Acids Research 30, 1575–1584 (2002)
Palla, G., et al.: Uncovering the Overlapping Community Structure of Complex Networks in Nature and Society. Nature 435, 814–818 (2005)
Adamcsek, B., et al.: CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics 22, 1021–1023 (2006)
Liu, G., Wong, L., Chua, H.N.: Complex discovery from weighted PPI networks. Bioinformatics 25, 1891–1897 (2009)
Chua, H.N., Sung, W.K., Wong, L.: Exploiting indirect neighbours and topological weight to predict protein function from protein–protein interactions. Bioinformatics 22, 1623–1630 (2006)
Jiang, P., Singh, M.: SPICi: a fast clustering algorithm for large biological networks. Bioinformatics 26, 1105–1111 (2010)
Wang, J.X., et al.: A Fast Hierarchical Clustering Algorithm for Functional Modules Discovery in Protein Interaction Networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics 8, 607–620 (2011)
Wu, M., et al.: A core-attachment based method to detect protein complexes in ppi networks. BMC Bioinformatics 10, 169 (2009)
Nepusz, T., Yu, H., Paccanaro, A.: Detecting overlapping protein complexes in protein-protein interaction networks. Nature Methods 9, 471–475 (2012)
Dezso, Z., Oltvai, Z.N., Barabási, A.L.: Analysis of Experimentally Determined Protein Complexes in the Yeast Saccharomyces cerevisiae. Genome Research 13, 2450–2454 (2003)
Gavin, A.C., et al.: Proteome survey reveals modularity of the yeast cell machinery. Genome Research 440, 631–636 (2006)
Li, X.L., Foo, C.S., Ng, S.K.: Discovering protein complexes in dense reliable neighborhoods of protein interaction networks. In: Proc. CSB, pp. 157–168 (2007)
Xenarios, X., et al.: DIP: the database of interacting proteins. Nucleic Acids Research 28, 289–291 (2000)
Krogan, N.J., et al.: Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440, 637–643 (2006)
Pu, S., et al.: Up-to-date catalogues of yeast protein complexes. Nucleic Acids Research 37, 825–831 (2009)
Boyle, E.I., et al.: GO:TermFinder-open source software for accessing GeneOntology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics 20, 3710–3715 (2004)
Hu, H., et al.: Mining coherent dense subgraphs across massive biological networks for functional discovery. Bioinformatics 25, 213–221 (2005)
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Zhao, B., Wang, J., Wu, FX., Pan, Y. (2013). Construction of Uncertain Protein-Protein Interaction Networks and Its Applications. In: Cai, Z., Eulenstein, O., Janies, D., Schwartz, D. (eds) Bioinformatics Research and Applications. ISBRA 2013. Lecture Notes in Computer Science(), vol 7875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38036-5_28
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DOI: https://doi.org/10.1007/978-3-642-38036-5_28
Publisher Name: Springer, Berlin, Heidelberg
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