An ILP Refinement Operator for Biological Grammar Learning

  • Daniel C. Fredouille
  • Christopher H. Bryant
  • Channa K. Jayawickreme
  • Steven Jupe
  • Simon Topp
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4455)


We are interested in using Inductive Logic Programming (ILP) to infer grammars representing sets of biological sequences. We call these biological grammars. ILP systems are well suited to this task in the sense that biological grammars have been represented as logic programs using the Definite Clause Grammar or the String Variable Grammar formalisms. However, the speed at which ILP systems can generate biological grammars has been shown to be a bottleneck. This paper presents a novel refinement operator implementation, specialised to infer biological grammars with ILP techniques. This implementation is shown to significantly speed-up inference times compared to the use of the classical refinement operator: time gains larger than 5-fold were observed in \(\frac{4}{5}\) of the experiments, and the maximum observed gain is over 300-fold.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Daniel C. Fredouille
    • 1
  • Christopher H. Bryant
    • 1
  • Channa K. Jayawickreme
    • 2
  • Steven Jupe
    • 3
  • Simon Topp
    • 4
  1. 1.School of Computing, The Robert Gordon University, AberdeenUK
  2. 2.Discovery Research Biology, GlaxoSmithKline, DurhamUSA
  3. 3.Department of Bioinformatics, GlaxoSmithKline, StevenageUK
  4. 4.Department of Bioinformatics, GlaxoSmithKline, HarlowUK

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