Abstract
The recent advent of high throughput methods has generated large amounts of protein-protein interaction network (PPIN) data. When studying the workings of a biological cell, it is useful to be able to detect known and predict still undiscovered protein complexes within the cell’s PPINs. Such predictions may be used as an inexpensive tool to direct biological experiments. Because of its importance in the studies of protein interaction network, there are different models and algorithms in identifying functional modules in PPINs. In this paper, we present two representative methods, focusing on the comparison of their clustering properties in PPIN and their contribution towards function prediction. The work is done with PPIN data from the bakers’ yeast (Saccaromyces cerevisiae) and since the network is noisy and still incomplete, we use pre-processing and purifying. As a conclusion new progress and future research directions are discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yu, G.X., Glass, E.M., Karonis, N.T., Maltsev, N.: Knowledge-based voting algorithm for automated protein functional annotation. PROTEINS: Structure, Function, and Bioinformatics 61, 907–917 (2005)
The gene ontology consortium, Gene ontology: Tool for the unification of biology. Nature Genetics 25(1), 25–29 (2000)
Spirin, V., Mirny, L.A.: Protein complexes and functional modules in molecular networks. PNASÂ 100, 21 (2003)
Bader, G.D., Hogue, C.W.V.: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics (4), 2 (2003)
Sharan, R., Ideker, T., Kelley, B., Shamir, R., Karp, R.M.: Identification of protein complexes by comparative analysis of yeast and bacterial protein interaction data. Computational Biology 12, 835–846 (2005)
Przulj, N., Wigle, D.A., Jurisica, I.: Functional Topology in a Network of Protein Interactions. Bioinformatics 20(3), 340–348 (2004)
Krogan, N.J., Cagney, G., Yu, H., Zhong, G., Guo, X., Ignatchenko, A.: Global Landscape of Protein Complexes in the Yeast Saccaromyces cerevisiae. Nature 440, 637–643 (2006)
Samanta, M.P., Liang, S.: Predicting protein functions from redundancies in large-scale protein interaction networks. PNASÂ 100, 22 (2003)
Brun, C., Chevenet, F., Martin, D., Wojcik, J., Guénoche, A., Jacq, B.: Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network. Genome. Biology 5, R6 (2003)
Chen, J., Yuan, B.: Detecting Functional Modules in the Yeast Protein-Protein Interaction Network. Bioinformatics 22(18), 2283–2290 (2006)
Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark Graphs for testing Community Detection Algorithms. Physical Review E78, 046110 (2008)
Rives, A.W., Galitski, T.: Modular Organization of cellular Networks. PNAS 10(3), 1128–1133 (2003)
Salwinski, L., Miller, C.S., Smith, A.J., Pettit, F.K., Bowie, J.U., Eisenberg, D.: The Database of Interacting Protein. Nucleic Acids Res. 32, D449–D451 (2004)
Guldener, U., Munsterkotter, M., Oesterheld, M., Ragel, P., Ruepp, A., Mewes, H.W.: MPact: the MIPS protein interaction resource on yeast. Nucleic Acids Res. 34, D436–D441 (2006)
Chatr-aryamontri, A., Ceol, A., Montecchi Palazzi, L., Nardelli, G., Schneider, M.V., Castagnoli, L., Cesareni, G.: MINT: the Molecular INTeraction database. Nucleic Acids Res. 35, D572–D574 (2007)
Bader, G.D., Hogue, C.W.V.: BIND–a data spec. for storing and describing biomolecular interactions, molecular complexes and pathways. Bioinformatics 16(5), 465–477 (2000)
Breitkreutz, B.J., Stark, C., Tyers, M.: The GRID: The General Repository for Interaction Datasets. Genome Biology 4(3), R23 (2003)
Dwight, S., Harris, M., Dolinski, K., Ball, C., BUnkley, G., Christie, K., Fisk, D., Issel-Tarver, L., Schroeder, M., Sherlock, G., Sethuraman, A., Weng, S., Botstein, D., Cherry, J.M.: Saccharomyces Genome Database (SGD) provides secondary gene annotation using Gene Ontology (GO). Nucleic Acids Research 30(1), 69–72 (2002)
Letovsky, S., Kasif, S.: Predicting protein function from protein/protein interaction data: a probabilistic approach. Bioinformatics 19, i197–i204 (2003)
Dunn, R., Dudbridge, F., Sanderson, C.M.: The Use of Edge-Betweenness Clustering to Investigate Biological Function in PIN. BMC Bioinformatics 6(1), 39 (2005)
Sen, Z., Kloczkowski, A., Jernigan, R.L.: Functional Clustering of Yeast Proteins from the Protein-Protein Interaction Network. BMC Bioinformatics 7, 355 (2006)
Girvan, M., Newman, M.E.J.: Community Structure in Social and Biological Networks. PNAS 99(12), 7821–7826 (2002)
Fortunato, S.: Community Detection in Graphs. Physics Reports 486, 75–174 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag GmbH Berlin Heidelberg
About this paper
Cite this paper
Cingovska, I., Bogojeska, A., Trivodaliev, K., Kalajdziski, S. (2012). Protein Function Prediction by Clustering of Protein-Protein Interaction Network. In: Kocarev, L. (eds) ICT Innovations 2011. ICT Innovations 2011. Advances in Intelligent and Soft Computing, vol 150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28664-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-28664-3_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28663-6
Online ISBN: 978-3-642-28664-3
eBook Packages: EngineeringEngineering (R0)