Abstract
With the development of high-throughput technologies in recent years, more and more scientists focus on protein-protein interaction (PPI) networks. Previous studies showed that there are modular structures in PPI networks. It is well known that Newman algorithm is a classical method for mining associations existed in complex networks, which has advantages of high accuracy and low complexity. Based on the Newman algorithm, we proposed an improved Newman algorithm to mine overlapping modules from PPI networks. Our method mainly consists of two steps. Firstly, we try to discover all candidate nodes whose neighbors belong to more than one module. Secondly, we determine candidate nodes that have positive effects on modularity as overlapping nodes and copy these nodes into their corresponding modules. In addition, owing to the features of existing system noise in PPI networks, we designed corresponding methods for de-noising. Experimental results concerning MIPS dataset show that, the proposed improved Newman algorithm not only has the ability of finding overlapping modular structure but also has low computational complexity.
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References
Schwikowski, B., Uetz, P., Fields, S.: A network of interacting proteins in yeast. Nature Biotechnology 18(12), 1257–1261 (2000)
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(1), 2 (2003)
Zhang, S.H., Ning, X.M., Zhang, X.S.: Identification of functional modules in a PPI network by clique percolation clustering. Computational Biology and Chemistry 30(6), 445–451 (2006)
Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Physical Review E 69(62), 066133-1–066133-5(2004)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69(22), 026113-1–026113-15 (2004)
Taylor, I.W., Linding, R., Warde, F.D.: Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nature Biotechnology 27(2), 199–204 (2009)
Kuchaiev, O., Rašajski, M., Higham, D.J., Pržulj, N.: Geometric de-noising of protein-protein interaction networks. PLoS Computational Biology 5(8), e1000454 (2009)
Asur, S., Ucar, D., Parthasarathy, S.: An ensemble framework for clustering protein-protein interaction networks. Bioinformatics 23(13), 29–40 (2007)
Danon, L., Diaz-Guilera, A., Duch, J., Arenas, A.: Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, P09008 (2005)
Mewes, H.W., Frishman, D., Mayer, K.F.: MIPS: analysis and annotation of proteins from whole genomes in 2005. Nucleic Acid Research 172, D169–D172 (2006)
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Wang, X., Li, L., Cheng, Y. (2012). An Improved Newman Algorithm for Mining Overlapping Modules from Protein-Protein Interaction Networks. In: Huang, DS., Gan, Y., Premaratne, P., Han, K. (eds) Bio-Inspired Computing and Applications. ICIC 2011. Lecture Notes in Computer Science(), vol 6840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24553-4_58
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DOI: https://doi.org/10.1007/978-3-642-24553-4_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24552-7
Online ISBN: 978-3-642-24553-4
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