Abstract
In current Proteomics, prediction of protein-protein interactions (PPI) is a crucial aim as these interactions take part in most essential biological processes. In this paper, we propose a new approach to PPI dataset processing based on the extraction information from well-known databases and the application of data mining techniques. This approach will provide very accurate Support Vector Machine models, trained using high-confidence positive and negative examples. Finally, our proposed model has been validated using experimental, computational and literature-collected datasets.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Braun, P., et al.: An experimentally derived confidence score for binary protein-protein interactions. Nat. Meth. 6(1), 91–97 (2009)
Chang, C., Lin, C.: LIBSVM: a Library for Support Vector Machines (2001), http://www.csie.ntu.edu.tw/cjlin/libsvm
Cortes, C., Vapnik, V.: Support vector network. Mach. Learn. (1995)
Gilad-Bachrach, A.N.R., Tishby, N.: Margin based feature selection: Theory and algorithms. In: Proc. of the 21’st ICML, pp. 43–50 (2004)
Huang, C., et al.: Predicting protein-protein interactions from protein domains using a set cover approach. IEEE/ACM Trans. Comput. Biol. Bioinform. 4(1) (2007)
Ito, T., et al.: A comprehensive two-hybrid analysis to explore the yeast protein interactome. PNAS 98(8), 4569–4574 (2001)
Jansen, R., et al.: A bayesian networks approach for predicting Protein-Protein interactions from genomic data. Science 302(5644), 449–453 (2003)
Jiang, T., Keating, A.E.: AVID: an integrative framework for discovering functional relationships among proteins. BMC Bioinformatics 6 (2005)
Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Proceedings 9th Int. Workshop on Machine Learning, pp. 249–256 (1992)
Patil, A., Nakamura, H.: Filtering high-throughput protein-protein interaction data using a combination of genomic features. BMC Bioinformatics 6(1), 100 (2005)
Saeed, R., Deane, C.: An assessment of the uses of homologous interactions. Bioinformatics 24(5), 689–695 (2008)
Uetz, P., et al.: A comprehensive analysis of protein-protein interactions in saccharomyces cerevisiae. Nature 403(6770), 623–627 (2000)
Wang, H., Azuaje, F., Bodenreider, O., Dopazo, J.: Gene expression correlation and gene ontology-based similarity: an assessment of quantitative relationships. In: CIBCB (2004)
Wu, X., et al.: Prediction of yeast protein-protein interaction network: insights from the gene ontology and annotations. Nucl. Acids Res. 34(7), 2137–2150 (2006)
Yu, H., et al.: High-Quality binary protein interaction map of the yeast interactome network. Science 322(5898), 104–110 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Urquiza, J.M., Rojas, I., Pomares, H., Herrera, L.J., Florido, J.P., Ortuño, F. (2011). Using Machine Learning Techniques and Genomic/Proteomic Information from Known Databases for PPI Prediction. In: Rocha, M.P., Rodríguez, J.M.C., Fdez-Riverola, F., Valencia, A. (eds) 5th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2011). Advances in Intelligent and Soft Computing, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19914-1_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-19914-1_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19913-4
Online ISBN: 978-3-642-19914-1
eBook Packages: EngineeringEngineering (R0)