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Statistically Based Pattern Discovery Techniques for Biological Data Analysis

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Applications of Computational Intelligence in Biology

Part of the book series: Studies in Computational Intelligence ((SCI,volume 122))

Summary

A statistically based pattern discovery tool is presented that produces a rule-based description of complex data through the set of its statistically significant associations. The rules resulting from this analysis capture all the patterns observable within a data set for which a statistically sound rationale is available. The validity of such patterns recommends their use in cases where the rationale underlying a decision must be understood. High-risk decision making systems, a milieu familiar to many biologically-related problem domains, is the likely area of application for this technique. An analysis of the performance of this technique on a series of biologically relevant data distributions is presented, and the relative merits and weaknesses of this technique are discussed.

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Author information

Authors and Affiliations

  1. School of Rehabilitation Therapy, Queen’s University, Canada

    Andrew Hamilton-Wright

  2. Computing and Information Science, University of Guelph, Canada

    Andrew Hamilton-Wright

  3. Mathematics and Computer Science, Mount Allison University, Canada

    Andrew Hamilton-Wright

  4. Systems Design Engineering, University of Waterloo, Canada

    Daniel W. Stashuk

Authors
  1. Andrew Hamilton-Wright
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  2. Daniel W. Stashuk
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Editor information

Editors and Affiliations

  1. Department of Biology, Emory University, 1510 Clifton Road NE, Atlanta, Georgia, 30322, USA

    Tomasz G. Smolinski

  2. Department of Computer Science, University of Arkansas at Little Rock, 2801 S. University Ave., Little Rock, Arkansas, 72204, USA

    Mariofanna G. Milanova

  3. Department of Quantitative Methods and Information Systems College of Business and Administration, Kuwait University, P.O. Box 5486, Safat, 13055, Kuwait

    Aboul-Ella Hassanien

  4. Department of Information Technology Faculty of Computers and Information, Cairo University, 5 Ahamed Zewal Street, Orman, Giza, Egypt

    Aboul-Ella Hassanien

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Hamilton-Wright, A., Stashuk, D.W. (2008). Statistically Based Pattern Discovery Techniques for Biological Data Analysis. In: Smolinski, T.G., Milanova, M.G., Hassanien, AE. (eds) Applications of Computational Intelligence in Biology. Studies in Computational Intelligence, vol 122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78534-7_1

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  • DOI: https://doi.org/10.1007/978-3-540-78534-7_1

  • Publisher Name: Springer, Berlin, Heidelberg

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  • Online ISBN: 978-3-540-78534-7

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