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Protein Motif Discovery with Linear Genetic Programming

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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

There have been published some studies of genetic programming as a way to discover motifs in proteins and other biological data. These studies have been small, and often used domain knowledge to improve search. In this paper we present a genetic programming algorithm, that does not use domain knowledge, with results on 44 different protein families. We demonstrate that our list-based representation, given a fixed amount of processing resources, is able to discover meaningful motifs with good classification performance. Sometimes comparable to or even surpassing that of motifs found in a database of manually created motifs. We also investigate introduction of gaps in our algorithm, and it seems that this give a small increase in classification accuracy and recall, but with reduced precision.

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Author information

Authors and Affiliations

  1. Norwegian University of Science and Technology, Sem Selandsv 7–9, NO-7034, Norway

    Rolv Seehuus

Authors
  1. Rolv Seehuus
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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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Seehuus, R. (2005). Protein Motif Discovery with Linear Genetic Programming. 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_109

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  • DOI: https://doi.org/10.1007/11553939_109

  • 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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