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Data Mining: Foundations and Intelligent Paradigms pp 63–99Cite as

Mining Biological Networks for Similar Patterns

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 25))

Abstract

In this chapter, we present efficient and accurate methods to analyze biological networks. Biological networks show how different biochemical entities interact with each other to perform vital functions for the survival of an organism. Three main types of biological networks are protein interaction networks, metabolic pathways and regulatory networks. In this work, we focus on alignment of metabolic networks.

We particularly focus on two algorithms which successfully tackle metabolic network alignment problem. The first algorithm uses a nonredundant graph model for representing networks. Using this model, it aligns reactions, compounds and enzymes of metabolic networks. The algorithm considers both the pairwise similarities of entities (homology) and the organization of networks (topology) for the final alignment. The second algorithm we describe allows mapping of entity sets to each other by relaxing the restriction of 1-to-1 mappings. This capturing biologically relevant alignments that cannot be identified by previous methods but it comes at an increasing computational cost and additional challenges. Finally, we discuss the significance of metabolic network alignment using the results of these algorithms on real data.

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Authors and Affiliations

  1. Computer and Information Science and Engineering, University of Florida, Gainesville, FL, 32611, USA

    Ferhat Ay, Günhan Gülsoy & Tamer Kahveci

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  1. Ferhat Ay
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  2. Günhan Gülsoy
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  3. Tamer Kahveci
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Correspondence to Ferhat Ay .

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Editors and Affiliations

  1. College of Letters & Science, Dept. Statistics & Applied Probability, University of California, Santa Barbara, South Hall 5504, Santa Barbara, 93106-3110, California, USA

    Dawn E. Holmes

  2. , School of Electrical and Information Eng, University of South Australia, Adelaide,, Mawson Lakes Campus, SA 5095, Australia

    Lakhmi C Jain

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Ay, F., Gülsoy, G., Kahveci, T. (2012). Mining Biological Networks for Similar Patterns. In: Holmes, D., Jain, L. (eds) Data Mining: Foundations and Intelligent Paradigms. Intelligent Systems Reference Library, vol 25. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23151-3_5

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  • DOI: https://doi.org/10.1007/978-3-642-23151-3_5

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