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Learning Chomsky-like grammars for biological sequence families

Muggleton, SH, Bryant, CH and Srinivasan, A 2000, 'Learning Chomsky-like grammars for biological sequence families' , in: Proceedings of the17th International Conference on Machine Learning , Morgan Kaufmann, San Francisco, CA, pp. 631-638.

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    Abstract

    This paper presents a new method of measuring performance when positives are rare and investigates whether Chomsky-like grammar representations are useful for learning accurate comprehensible predictors of members of biological sequence families. The positive-only learning framework of the Inductive Logic Programming (ILP) system CProgol is used to generate a grammar for recognising a class of proteins known as human neuropeptide precursors (NPPs). As far as these authors are aware, this is both the first biological grammar learnt using ILP and the first real-world scientific application of the positive-only learning framework of CProgol. Performance is measured using both predictive accuracy and a new cost function, em Relative Advantage (RA). The RA results show that searching for NPPs by using our best NPP predictor as a filter is more than 100 times more efficient than randomly selecting proteins for synthesis and testing them for biological activity. The highest RA was achieved by a model which includes grammar-derived features. This RA is significantly higher than the best RA achieved without the use of the grammar-derived features.

    Item Type: Book Section
    Editors: Langley, P
    Themes: Subjects / Themes > Q Science > QA Mathematics > QA075 Electronic computers. Computer science
    Subjects / Themes > Q Science > QH Natural history > QH301 Biology
    Subjects outside of the University Themes
    Schools: Colleges and Schools > College of Science & Technology
    Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering
    Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > Data Mining and Pattern Recognition Research Centre
    Publisher: Morgan Kaufmann
    Refereed: Yes
    ISBN: 1-55860-707-2
    Related URLs:
    Depositing User: Dr Chris H. Bryant
    Date Deposited: 16 Feb 2009 16:05
    Last Modified: 20 Aug 2013 16:56
    URI: http://usir.salford.ac.uk/id/eprint/1763

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