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Fredouille, DC; Bryant, CH; Jayawickreme, CK; Jupe, S; Topp, S
Publisher: Springer
Languages: English
Types: Part of book or chapter of book
Subjects: other, QH301, QA75

Classified by OpenAIRE into

ACM Ref: TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES
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 4/5 of the experiments, and the maximum observed gain is over 300-fold.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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