Abstract
Mining functional modules in a Protein-Protein Interaction (PPI) network contributes greatly to the understanding of biological mechanism, where how to effectively detect functional modules in a PPI network has a significant application. As a meta-heuristic and stochastic search technology, the Ant Colony Optimization (ACO) algorithm has been one of the effective tools for solving discrete optimization problems. In this paper, we propose a new method based on the ACO algorithm for detecting functional modules in a PPI network, which combines topological characteristics with functional information. First, a new heuristic function is introduced to conduct ants searching effectively in constructing solutions. Second, a set of new strategies of partitioning, merging and filtering are adopted to form the final functional modules. Finally, we present experimental results on the benchmark testing set of yeast networks. Our experiments show that our approach is more effective compared to several other existing detection techniques.
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
Dorigo, M., Maniezzo, V.A., Colorni: The ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B 26(1), 29–41 (1996)
Blum, C., Dorigo, M.: Search bias in Ant Colony Optimization: On the role of competition-balanced systems. IEEE Transactions Evolutionary Computation 9(2), 159–174 (2005)
Angus, D., Woodward, C.: Multiple objective ant colony optimization. Swarm Intelligence, Special Issue on Ant Colony Optimization 3(1), 69–85 (2009)
Zhang, A.D.: Protein Interaction Networks: Computational Analysis. Cambridge University Press (2009)
Hwang, W., Cho, Y.R., Zhang, A.D., Ramanathan, M.: A novel functional module detection algorithm for protein-protein interaction networks. Algorithms for Molecular Biology 1, 24 (2006)
Guimera, R., Nunes Amaral, L.: Functional cartography of complex metabolic networks. Nature 433, 895–900 (2005)
Spirin, V., Mirny, L.A.: Protein complexes and functional modules in molecular networks. Proceedings of the National Academy of Sciences 100(21), 12123–12128 (2003)
Bader, G.D., Hogue, C.W.: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4(2) (2003)
Pei, P., Zhang, A.D.: A ”seed-refine” algorithm for detecting protein complexes from protein interaction data. IEEE Transactions on Nanobioscience 6(1), 43–50 (2007)
Ravasz, E., Somera, A.L., Mongru, D.A., Oltvai, Z.N., Barabasi, A.L.: Hierarchical organization of modularity in metabolic networks. Science 297, 1551–1555 (2002)
King, A.D., Przulj, N., Jurisica, I.: Protein complex prediction via cost-based clustering. Bioinformatics 20(17), 3013–3020 (2004)
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 15(5814), 972–976 (2007)
Hwang, W., Cho, Y.R., Zhang, A.D., Ramanathan, M.: CASCADE: a novel quasi all paths-based network analysis algorithm for clustering biological interactions. BMC Bioinformatics 9, 64 (2008)
Sallim, J., Abdullah, R., Khader, A.T.: ACOPIN: An ACO Algorithm with TSP Approach for Clustering Proteins from Protein Interaction Network. In: Second UKSIM European Symposium on Computer Modeling and Simulation, pp. 203–208 (2008)
Sallim, J., Abdullah, R., Khader, A.T.: An imprvoved ant colony optimization algorithm for clustering proteins in Protein Interaction Network. In: International Conference on Software Engineering & Computer Systems, ICSECS 2009 (2009)
Brun, C., Herrmann, C., Gunoche, A.: Clustering proteins from interaction networks for the prediction of cellular functions. BMC Bioinformatics 5, 95 (2004)
Schlicker, A., Albrecht, M.: FunSimMat: a comprehensive functional similarity database. Nucleic Acids Research 36, D434–D439 (2008)
Friedel, C.C., Krumsiek, J., Zimmer, R.: Bootstrapping the Interactome: Unsupervised Identification of Protein Complexes in Yeast. In: Vingron, M., Wong, L. (eds.) RECOMB 2008. LNCS (LNBI), vol. 4955, pp. 3–16. Springer, Heidelberg (2008)
Mewes, H.W., et al.: MIPS: analysis and annotation of proteins from whole genomes. Nucleic Acids Res. 32(database issue), D41–D44 (2004)
Aloy, P., et al.: Structure-based assembly of protein complexes in yeast. Science 303(5666), 2026–2029 (2004)
Dwight, S.S., et al.: Saccharomyces Genome Database provides secondary gene annotation using the Gene Ontology. Nucleic Acids Reasearch 30(1), 69–72 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ji, J., Liu, Z., Zhang, A., Jiao, L., Liu, C. (2012). Improved Ant Colony Optimization for Detecting Functional Modules in Protein-Protein Interaction Networks. In: Liu, C., Wang, L., Yang, A. (eds) Information Computing and Applications. ICICA 2012. Communications in Computer and Information Science, vol 308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34041-3_57
Download citation
DOI: https://doi.org/10.1007/978-3-642-34041-3_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34040-6
Online ISBN: 978-3-642-34041-3
eBook Packages: Computer ScienceComputer Science (R0)