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Learning Information Patterns in Biological Databases - Stochastic Data Mining

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Data Mining and Knowledge Discovery Handbook

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This chapter aims at developing the computational theory for modeling patterns and their hierarchical coordination within biological sequences. With the exception of the promoters and enhancers, the functional significance of the non-coding DNA is not well understood. Scientists are now discovering that specific regions of non-coding DNA interact with the cellular machinery and help bring about the expression of genes. Our premise is that it is possible to study the arrangements of patterns in biological sequences through machine learning algorithms. As the biological database continue their exponential growth, it becomes feasible to apply in-silico Data Mining algorithms to discover interesting patterns of motif arrangements and the frequency of their re-iteration. A systematic procedure for achieving this goal is presented.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Center for Bioinformatics, Oakland University, Rochester, MI, 48309, USA

    Gautam B. Singh

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  1. Gautam B. Singh
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Editors and Affiliations

  1. , Dept. Industrial Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Oded Maimon

  2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Lior Rokach

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Singh, G.B. (2009). Learning Information Patterns in Biological Databases - Stochastic Data Mining. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09823-4_59

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  • DOI: https://doi.org/10.1007/978-0-387-09823-4_59

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-09822-7

  • Online ISBN: 978-0-387-09823-4

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