The Community for Technology Leaders
RSS Icon
Issue No.06 - Nov.-Dec. (2012 vol.9)
pp: 1790-1804
M. S. Amin , Dept. of Comput. Sci., Wayne State Univ., Sunnyvale, CA, USA
R. L. Finley , Center for Mol. Med. & Genetics, Wayne State Univ., Detroit, MI, USA
H. M. Jamil , Dept. of Comput. Sci., Univ. of Idaho, Moscow, ID, USA
Many emerging database applications entail sophisticated graph-based query manipulation, predominantly evident in large-scale scientific applications. To access the information embedded in graphs, efficient graph matching tools and algorithms have become of prime importance. Although the prohibitively expensive time complexity associated with exact subgraph isomorphism techniques has limited its efficacy in the application domain, approximate yet efficient graph matching techniques have received much attention due to their pragmatic applicability. Since public domain databases are noisy and incomplete in nature, inexact graph matching techniques have proven to be more promising in terms of inferring knowledge from numerous structural data repositories. In this paper, we propose a novel technique called TraM for approximate graph matching that off-loads a significant amount of its processing on to the database making the approach viable for large graphs. Moreover, the vector space embedding of the graphs and efficient filtration of the search space enables computation of approximate graph similarity at a throw-away cost. We annotate nodes of the query graphs by means of their global topological properties and compare them with neighborhood biased segments of the data-graph for proper matches. We have conducted experiments on several real data sets, and have demonstrated the effectiveness and efficiency of the proposed method.
Proteins, Databases, Graphics, Bioinformatics, Computational biology, Diseases, Bioinformatics, Genetics,biology and genetics, Graphs and networks, knowledge and data engineering tools and techniques, bioinformatics, graph and tree search strategies
M. S. Amin, R. L. Finley, H. M. Jamil, "Top-k Similar Graph Matching Using TraM in Biological Networks", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 6, pp. 1790-1804, Nov.-Dec. 2012, doi:10.1109/TCBB.2012.90
[1] D. Shasha, J.T.L. Wang, and R. Giugno, “Algorithmics and Applications of Tree and Graph Searching,” Proc. 21st ACM SIGMOD-SIGACT-SIGART Symp. Principles of Database Systems (PODS), pp. 39-52, 2002.
[2] X. Yan, P.S. Yu, and J. Han, “Graph Indexing: A Frequent Structure-Based Approach,” Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 335-346, 2004.
[3] X. Yan, F. Zhu, J. Han, and P.S. Yu, “Searching Substructures with Superimposed Distance,” Proc. IEEE 22nd Int'l Conf. Data Eng. (ICDE), p. 88, 2006.
[4] D.W. Williams, J. Huan, and W. Wang, “Graph Database Indexing Using Structured Graph Decomposition,” Proc. IEEE 23rd Int'l Conf. Data Eng. (ICDE), pp. 976-985, 2007.
[5] H. Jiang, H. Wang, P.S. Yu, and S. Zhou, “Gstring: A Novel Approach for Efficient Search in Graph Databases,” Proc. IEEE 23rd Int'l Conf. Data Eng. (ICDE), pp. 566-575, 2007.
[6] X. Yan, P.S. Yu, and J. Han, “Substructure Similarity Search in Graph Databases,” Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 766-777, 2005.
[7] G.T. Hart, A. Ramani, and E. Marcotte, “How Complete Are Current Yeast and Human Protein-Interaction Networks?” Genome Biology, vol. 7, no. 11, pp. 120.1-120.9, Dec. 2006.
[8] K. Venkatesan, J.-F.F. Rual, A. Vazquez, U. Stelzl, I. Lemmens, T. Hirozane-Kishikawa, T. Hao, M. Zenkner, X. Xin, K.-I. I. Goh, M.A. Yildirim, N. Simonis, K. Heinzmann, F. Gebreab, J.M. Sahalie, S. Cevik, C. Simon, A.-S.S. de Smet, E. Dann, A. Smolyar, A. Vinayagam, H. Yu, D. Szeto, H. Borick, A. Dricot, N. Klitgord, R.R. Murray, C. Lin, M. Lalowski, J. Timm, K. Rau, C. Boone, P. Braun, M.E. Cusick, F.P. Roth, D.E. Hill, J. Tavernier, E.E. Wanker, A.-L.L. Barabási, and M. Vidal, “An Empirical Framework for Binary Interactome Mapping,” Nature Methods, vol. 6, no. 1, pp. 83-90, Jan. 2009.
[9] P. Uetz and R.L. Finley, “From Protein Networks to Biological Systems,” FEBS Letters, vol. 579, no. 8, pp. 1821-1827, Mar. 2005.
[10] M.E. Cusick, N. Klitgord, M. Vidal, and D.E. Hill, “Interactome: Gateway into Systems Biology,” Human Molecular Genetics, vol. 14, Special no. 2, pp. R171-R181, Oct. 2005.
[11] B.-J. Breitkreutz, C. Stark, T. Reguly, L. Boucher, A. Breitkreutz, M. Livstone, R. Oughtred, D.H. Lackner, J. Bähler, V. Wood, K. Dolinski, and M. Tyers, “The BioGRID Interaction Database: 2008 Update,” Nucleic Acids Research, vol. 36, pp. D637-D640, Jan. 2008.
[12] T. Murali, S. Pacifico, J. Yu, S. Guest, G. Roberts, and F. RLJ, “DroID 2011: A Comprehensive Integrated Reources for Protein, Transcription Factor, RNA, and Gene Interactions for Drosophila,” Nucleic Acids Research, vol. 39, database issue, pp. D736-D743, 2011.
[13] M.P. Samanta and S. Liang, “Predicting Protein Functions from Redundancies in Large-Scale Protein Interaction Networks,” Proc. Nat'l Academy of Sciences USA, vol. 100, no. 22, pp. 12579-12583, Oct. 2003.
[14] B. Schwikowski, P. Uetz, and S. Fields, “A Network of Protein-Protein Interactions in Yeast,” Nature Biotechnology, vol. 18, no. 12, pp. 1257-1261, Dec. 2000.
62 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool