Abstract
Protein-protein interaction networks have been broadly studied in the last few years, in order to understand the behavior of proteins inside the cell. Proteins interacting with each other often share common biological functions or they participate in the same biological process. Thus, discovering protein complexes made of groups of proteins strictly related, can be useful to predict protein functions. Clustering techniques have been widely employed to detect significative biological complexes. In this paper, we integrate one of the most popular network clustering techniques, namely the Restricted Neighborhood Search Clustering (RNSC), with evolutionary computation. The two cost functions introduced by RNSC, besides a new one that combines them, are used by a Genetic Algorithm as fitness functions to be optimized. Experimental evaluations performed on two different groups of interactions of the budding yeast Saccaromices cerevisiae show that the clusters obtained by the genetic approach are more accurate than those found by RNSC, though this method predicts more true complexes.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Altaf-Ul-Amin, M., Shinbo, Y., Mihara, K., Kurokawa, K., Kanaya, S.: Development and implementation of an algorithm for detection of protein complexes in large interaction networks. BMC Bioinformatics 7(207) (2006)
Atias, N., Sharan, R.: Comparative analysis of protein networks: hard problems, practical solutions. Commun. ACM 55(5), 88–97 (2012)
Bader, G., Hogue, H.: An automated method for finding molecular complexes in large protein-protein interaction networks. BMC Bioinformatics 4(2) (2003)
Barabási, A., Oltvai, Z.N.: Network biology: Understanding the cell’s functional organization. Nature Review Genetics 5, 101–113 (2004)
Blatt, M., Wiseman, S., Domany, E.: Superparamagnetic clustering of data. Phisical Review Letters 76(18), 3251–3254 (1996)
Brohèe, S., van Helden, J.: Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinformatics 7, 488 (2006)
Cho, Y.-R., Hwang, W., Ramanathan, M., Zhang, A.: Semantic integration to identify overlapping functional modules in protein interaction networks. BMC Bioinformatics 8, 265 (2007)
Thomas, H., Cormen, C.E., Leiserson, R.L.: Rivest, and Clifford Stein. In: Introduction to Algorithms, 2nd edn. MIT Press (2007)
Farutin, V., Robinson, K., Lightcap, E., Dancik, V., Ruttenberg, A., Letovsky, S., Pradines, J.: Edge-count probabilities for the identification of local protein communities and their organization. Proteins: Structure, Function, and Bioinformatics 62, 800–818 (2006)
Ferraro, N., Palopoli, L., Panni, S., Rombo, S.E.: Asymmetric comparison and querying of biological networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics 8, 876–889 (2011)
Gavin, A.C., et al.: Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631–636 (2006)
Hartwell, L.H., Hopfield, J.J., Leibler, S., Murray, A.W.: Clustering algorithm based graph connectivity. Nature 402, 47–52 (1999)
Hwang, W., Cho, Y.-R., Zhang, A., Ramanathan, M.: A novel functional module detection algorithm for protein-protein interaction networks. Algorithms for Molecular Biology 1(24) (2006)
King, A.D., Przulj, N., Jurisica, I.: Protein complex prediction via cost-based clustering. Bioinformatics 20(17), 3013–3020 (2004)
Krogan, N.J., et al.: Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440(7084), 637–643 (2006)
Li, M., Chen, J., Wang, J., Hu, B., Chen, G.: Modifying the DPClus algorithm for identifying protein complexes based on new topological structures. BMC Bioinformatics 9 (2008)
Lin, C., Cho, Y., Hwang, W., Pei, P., Zhang, A.: Clustering methods in protein-protein interaction network. Knowledge Discovery in Bioinformatics: Techniques, Methods and Application. John Wiley & Sons, Inc. (2006)
Liu, H., Liu, J.: Clustering protein interaction data through chaotic genetic algorithm. In: Wang, T.-D., Li, X., Chen, S.-H., Wang, X., Abbass, H.A., Iba, H., Chen, G.-L., Yao, X. (eds.) SEAL 2006. LNCS, vol. 4247, pp. 858–864. Springer, Heidelberg (2006)
Mewes, H.W., et al.: MIPS: a database for genomes and protein sequences. Nucleic Acids Res. 30(1), 31–34 (2002)
Mewes, H.W., et al: MIPS: analysis and annotation of proteins from whole genomes in 2005. Nucleic Acids Res. 34(database issue(1), 169–172 (2006)
Miller, J.P., et al.: Large-scale identification of yeast integral membrane protein interactions. Proc. Natl. Acad. Sci. USA 102(34), 12123–12128 (2005)
Moschopoulos, C.N., Pavlopoulos, P.A., Iacucci, E., Aerts, J., Likothanassis, S., Schneider, R., Kossida, S.: Which clustering algorithm is better for predicting protein complexes? BMC Research Notes 4(549) (2011)
Palla, G., Derenyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)
Park, Y.J., Song, M.S.: A genetic algorithm for clustering problems. In: Proc. of 3rd Annual Conference on Genetic Algorithms, pp. 2–9 (1989)
Pei, P., Zhang, A.: A two-step approach for clustering proteins based on protein interaction profiles. In: IEEE Int. Symposium on Bioinformatics and Bioengeneering (BIBE 2005), pp. 201–209 (2005)
Pereira, J.B., Enright, A.J., Ouzounis, C.A.: Detection of functional modules from protein interaction networks. Proteins: Structure, Fuctions, and Bioinformatics (20), 49–57 (2004)
Pizzuti, C., Rombo, S.E.: Discovering Protein Complexes in Protein Interaction Networks in Biological Data Mining in Protein Interaction Networks. In: Li, X.-L., Ng, S.-K. (eds.). IGI Global- Medical Inf. Science Ref. (2009)
Pizzuti, C., Rombo, S.E.: A coclustering approach for mining large protein-protein interaction networks. IEEE/ACM Trans. Comput. Biology Bioinform. 9(3), 717–730 (2012)
Pizzuti, C., Rombo, S.E., Marchiori, E.: Complex detection in protein-protein interaction networks: A compact overview for researchers and practitioners. In: Giacobini, M., Vanneschi, L., Bush, W.S. (eds.) EvoBIO 2012. LNCS, vol. 7246, pp. 211–223. Springer, Heidelberg (2012)
Pizzuti, C., Rombo, S.E.: Experimental evaluation of topological-based fitness functions to detect complexes in PPI networks. In: Proc. of the Genetic and Evolutionary Computation Conference (Gecco 2012), pp. 193–200 (2012)
Ravaee, H., Masoudi-Nejad, A., Omidi, S., Moeini, A.: Improved immune genetic algorithm for clustering protein-protein interaction network. In: Proceedings of the 2010 IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2010, pp. 174–179. IEEE Computer Society (2010)
Samantha, M.P., Liang, S.: Predicting protein functions from redundancies in large-scale protein interaction networks. Proc. of the National Academy of Science 100(22), 12579–12583 (2003)
Sharan, R., Ulitsky, I., Shamir, R.: Network-based prediction of protein function. Molecular Systems Biology 3(88) (2007)
Spirin, V., Mirny, L.A.: Protein complexes and functional modules in molecular networks. PNAS 100, 12123–12128 (2003)
Tornw, S., Mewes, H.W.: Functional modules by relating protein interaction networks and gene expression. Nucleic Acids Research 31(21), 6283–6289 (2003)
De Virgilio, R., Rombo, S.E.: Approximate matching over biological RDF graphs. In: Proceedings of the ACM Symposium on Applied Computing, SAC 2012, pp. 1413–1414 (2012)
von Mering, D., Krause, C., et al.: Comparative assessment of a large-scale data sets of protein-protein interactions. Nature 31, 399–403 (2002)
Wang, J., Li, M., Deng, Y., Pan, Y.: Recent advances in clustering methods for protein interaction networks. BMC Genomics 11(suppl. 3), S10 (2010)
Zaki, N., Berengueres, J., Efimov, D.: Prorank: a method for detecting protein complexes. In: Proc. of the Genetic and Evolutionary Computation Conference (Gecco 2012), pp. 209–216 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pizzuti, C., Rombo, S.E. (2013). Restricted Neighborhood Search Clustering Revisited: An Evolutionary Computation Perspective. In: Ngom, A., Formenti, E., Hao, JK., Zhao, XM., van Laarhoven, T. (eds) Pattern Recognition in Bioinformatics. PRIB 2013. Lecture Notes in Computer Science(), vol 7986. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39159-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-39159-0_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39158-3
Online ISBN: 978-3-642-39159-0
eBook Packages: Computer ScienceComputer Science (R0)