Abstract
Systems biology has become a major field of post-genomic bioinformatics research. A biological network containing various objects and their relationships is a fundamental way to represent a bio-system. A graph consisting of vertices and edges between these vertices is a natural data structure to represent biological networks. Substructure analysis of metabolic pathways by graph-based relational learning provides us biologically meaningful substructures for system-level understanding of organisms.
This chapter presents a graph representation of metabolic pathways to describe all features of metabolic pathways and describes the application of graph-based relational learning for structure analysis on metabolic pathways in both supervised and unsupervised scenarios. We show that the learned substructures can not only distinguish between two kinds of biological networks and generate hierarchical clusters for better understanding of them, but also have important biological meaning.
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
Aittokallio, T., Schwikowski, B.: Graph-based methods for analysing networks in cell biology. Briefings in Bioinformatics 7(3), 243–255 (2006)
Bu, D., Zhao, Y., Cai, L., et al.: Topological structure analysis of the protein-protein interaction network in budding yeast. Nucleic Acids Research 31, 2443–2450 (2003)
Cheng, C.Y., Huang, C.Y., Sun, C.T.: Mining bridge and brick motifs from complex biological networks for functionally and statistically significant discovery. IEEE Transactions on Systems, Man, and Cybernetics, Part B 38(1), 17–24 (2008)
Chittimoori, R., Holder, L., Cook, D.: Applying the subdue substructure discovery system to the chemical toxicity domain. In: Proceedings of the Florida AI Research Symposium, pp. 90–94 (1999)
Cook, D., Holder, L.: Substructure discovery using minimum description length and background knowledge. Journal of Artificial Intelligence Research 1, 231–255 (1994)
Cook, D., Manocha, N., Holder, L.: Using a graph-based data mining system to perform web search. International Journal of Pattern Recognition and Artificial Intelligence 17(5) (2003)
Dzerosk, S.: Multi-relational data mining: an introduction. SIGKDD Explorations Newsletter 5(1), 1–16 (2003)
Gonzalez, J., Holder, L., Cook, D.: Graph-based relational concept learning. In: Proceedings of the International Conference on Machine Learning, pp. 219–226 (2002)
Holder, L., Cook, D., Coble, J., Mukherjee, M.: Graph-based relational learning with application to security. Fundamenta Informaticae Special Issue on Mining Graphs, Trees and Sequences 6, 83–101 (2005)
Holder, L., Cook, D., Gonzalez, J., Jonyer, I.: Structural Pattern Recognition in Graphs. In: Pattern Recognition and String Matching, pp. 255–280. Springer, Heidelberg (2003)
Hu, H., Yan, X., Huang, Y., Han, J., Zhou, X.J.: Mining coherent dense subgraphs across massive biological networks for functional discovery. Bioinformatics 21(1), 213–221 (2005)
Huan, J., Wang, W., Bandyopadhyay, D., Snoeyink, J., Prins, J., Tropsha, A.: Mining protein family specific residue packing patterns from protein structure graphs. In: Eighth Annual International Conference on Research in Computational Molecular Biology (RECOMB), pp. 308–315 (2004)
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 (2006)
Kanehisa, M., Goto, S., Kawashima, S., Okuno, U., Hattori, M.: KEGG resource for deciphering the genome. Nucleic Acids Research 32, 277–280 (2004)
Kitano, H.: Systems biology: A brief overview. Science 295, 1662–1664 (2002)
Klipp, E., Herwig, R., Kowald, A., Wierling, C., Lehrach, H.: Systems Biology in Practice: Concepts, Implementation and Application, 1st edn. WILEY-VCH, Weinheim (2005)
Koyuturk, M., Grama, A., Szpankowski, W.: An efficient algorithm for detecting frequent subgraphs in biological networks. In: Proceedings of the International Conference on Intelligent Systems for Molecular Biology, vol. 20, pp. 200–207 (2004)
Kukluk, J., You, C., Holder, L., Cook, D.: Learning node replacement graph grammars in metabolic pathways. In: Proceedings of International Conference on Bioinformatics and Computational Biology, BIOCOMP 2007 (2007)
Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: Proceedings of the IEEE Conference on Data Mining, pp. 313–320 (2001)
Laub, M., Loomis, W.: A molecular network that produces spontaneous oscillations in excitable cells of dictyostelium. Mol. Biol. Cell 9(12), 3521–3532 (1998)
Lodhi, H., Muggleton, S.H.: Modelling metabolic pathways using stochastic logic programs-based ensemble methods. In: Danos, V., Schachter, V. (eds.) CMSB 2004. LNCS (LNBI), vol. 3082, pp. 119–133. Springer, Heidelberg (2005)
Muggleton, S.: Inductive logic programming. New Generation Computing 8, 295–318 (1991)
Muggleton, S.H., Lodhi, H., Amini, A., Sternberg, M.J.E.: Support Vector Inductive Logic Programming. In: Hoffmann, A., Motoda, H., Scheffer, T. (eds.) DS 2005. LNCS, vol. 3735, pp. 163–175. Springer, Heidelberg (2005)
Su, S., Cook, D., Holder, L.: Application of knowledge discovery to molecular biology: Identifying structural regularities in proteins. In: Proceedings of the Pacific Symposium on Biocomputing, vol. 4, pp. 190–201 (1999)
Tamaddoni-Nezhad, A., Kakas, A., Muggleton, S., Pazos, F.: Modelling inhibition in metabolic pathways through abduction and induction. In: Camacho, R., King, R., Srinivasan, A. (eds.) ILP 2004. LNCS, vol. 3194, pp. 305–322. Springer, Heidelberg (2004)
KEGG, http://www.genome.jp
Wolf, J., Sohn, H., Heinrich, R., Kuriyama, H.: Mathematical analysis of a mechanism for autonomous metabolic oscillations in continuous culture of saccharomyces cerevisiae. FEBS Lett. 499(3), 230–234 (2001)
Yan, X., Han, J.: Gspan: Graph-based substructure pattern mining. In: Proceedings of the IEEE Conference on Data Mining, pp. 721–724 (2002)
You, C., Holder, L., Cook, D.: Graph-based data mining in dynamic networks: Empirical comparison of compression-based and frequency-based subgraph mining. In: IEEE International Conference on Data Mining (ICDM) Workshop on Analysis of Dynamic Networks (2008)
You, C., Holder, L., Cook, D.: Graph-based temporal mining of metabolic pathways with microarray data. In: ACM SIGKDD Workshop on Data Mining in Bioinformatics, BIOKDD (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
You, C.h., Holder, L.B., Cook, D.J. (2009). Substructure Analysis of Metabolic Pathways by Graph-Based Relational Learning. In: Sidhu, A.S., Dillon, T.S. (eds) Biomedical Data and Applications. Studies in Computational Intelligence, vol 224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02193-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-02193-0_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02192-3
Online ISBN: 978-3-642-02193-0
eBook Packages: EngineeringEngineering (R0)