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Generalized Composite Motif Discovery

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3683))

Abstract

This paper discusses a general algorithm for the discovery of motif combinations. From a large number of input motifs, discovered by any single motif discovery tool, our algorithm discovers sets of motifs that occur together in sequences from a positive data set. Generality is achieved by working on occurrence sets of the motifs. The output of the algorithm is a Pareto front of composite motifs with respect to both support and significance. We have used our method to discover composite motifs for the AlkB family of homologues. Some of the returned motifs confirm previously known conserved patterns, while other sets of strongly conserved patterns may characterize subfamilies of AlkB.

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Author information

Authors and Affiliations

  1. Norwegian University of Science and Technology, 7052, Trondheim, Norway

    Geir Kjetil Sandve & Finn Drabløs

Authors
  1. Geir Kjetil Sandve
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  2. Finn Drabløs
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Editor information

Editors and Affiliations

  1. School of Business, La Trobe University, 3086, Melbourne, Victoria, Australia

    Rajiv Khosla

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2005 Springer-Verlag Berlin Heidelberg

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Sandve, G.K., Drabløs, F. (2005). Generalized Composite Motif Discovery. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553939_108

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  • DOI: https://doi.org/10.1007/11553939_108

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28896-1

  • Online ISBN: 978-3-540-31990-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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