Abstract
Network motifs, patterns of local interconnections with potential functional properties, are important for the analysis of biological networks. To analyse motifs in networks the first step is to find patterns of interest. This paper presents 1) three different concepts for the determination of pattern frequency and 2) an algorithm to compute these frequencies. The different concepts of pattern frequency depend on the reuse of network elements. The presented algorithm finds all or highly frequent patterns under consideration of these concepts. The utility of this method is demonstrated by applying it to biological data.
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References
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: Simple building blocks of complex networks. Science 298, 824–827 (2002)
Shen-Orr, S., Milo, R., Mangan, S., Alon, U.: Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetices 31, 64–68 (2002)
Wuchty, S., Oltvai, Z.N., Barabási, A.L.: Evolutionary conservation of motif constituents in the yeast protein interaction network. Nature Genetics 35, 176–179 (2003)
Mangan, S., Alon, U.: Structure and function of the feed-forward loop network motif. In: Proceedings of the National Academy of Sciences, vol. 100, pp. 11980–11985 (2003)
Inokuchi, A., Washio, T., Motoda, H.: Complete mining of frequent patterns from graphs: Mining graph data. Machine Learning 50, 321–354 (2003)
Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: IEEE International Conference on Data Mining (ICDM), pp. 313–320 (2001)
Yan, X., Han, J.: gspan: Graph-based substructure pattern mining. In: IEEE International Conference on Data Mining (ICDM), pp. 721–724 (2002)
Kuramochi, M., Karypis, G.: Finding frequent patterns in a large sparse graph. In: SIAM International Conference on Data Mining, SDM 2004 (2004)
Vanetik, N., Gudes, E., Shimony, S.E.: Computing frequent graph patterns from semistructured data. In: IEEE International Conference on Data Mining (ICDM), pp. 458–465 (2002)
Harary, F., Palmer, E.M.: Graphical Enumeration. Academic Press, New York (1973)
Kuramochi, M., Karypis, G.: An efficient algorithm for discovering frequent subgraphs. Technical Report 02-026, Department of Computer Science, University of Minnesota (2002)
Garey, M., Johnson, D.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman and Company, New York (1979)
Lee, T.I., Rinaldi, N.J., Robert, F., Odom, D.T., Bar-Joseph, Z., Gerber, G.K., Hannett, N.M., Harbison, C.T., Thompson, C.M., Simon, I., Zeitlinger, J., Jennings, E.G., Murray, H.L., Gordon, D.B., Ren, B., Wyrick, J.J., Tagne, J.B., Volkert, T.L., Fraenkel, E., Gifford, D.K., Young, R.A.: Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298, 799–804 (2002)
Ma, H., Zeng, A.P.: Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms. Bioinformatics 19, 270–277 (2003)
Srinivasan, A., King, R.D., Muggleton, S.H., Sternberg, M.J.E.: The predictive toxicology evaluation challenge. In: 15th International Joint Conference on Artificial Intelligence (IJCAI), pp. 1–6 (1997)
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Schreiber, F., Schwöbbermeyer, H. (2005). Frequency Concepts and Pattern Detection for the Analysis of Motifs in Networks. In: Priami, C., Merelli, E., Gonzalez, P., Omicini, A. (eds) Transactions on Computational Systems Biology III. Lecture Notes in Computer Science(), vol 3737. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11599128_7
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DOI: https://doi.org/10.1007/11599128_7
Publisher Name: Springer, Berlin, Heidelberg
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