Mining Biological Networks for Similar Patterns

Ferhat Ay*, Günhan Gülsoy, Tamer Kahveci

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapter

    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.

    Original languageEnglish (US)
    Title of host publicationData Mining
    Subtitle of host publicationFoundations and Intelligent Paradigms: Volume 3:Medical,Health, Social, Biological and other Applications
    EditorsDawn Holmes, Lakhmi Jain
    Pages63-99
    Number of pages37
    DOIs
    StatePublished - Dec 1 2012

    Publication series

    NameIntelligent Systems Reference Library
    Volume25
    ISSN (Print)1868-4394
    ISSN (Electronic)1868-4408

    ASJC Scopus subject areas

    • Computer Science(all)
    • Information Systems and Management
    • Library and Information Sciences

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