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Pertinent background knowledge for learning protein grammars

Bryant, CH, Fredouille, DC, Wilson, A, Jayawickreme, CK, Jupe, S and Topp, S 2006, 'Pertinent background knowledge for learning protein grammars' , in: Machine learning: ECML 2006 , Lecture notes in artificial intelligence (subseries of Lecture notes in computer science) (4212) , Springer, Berlin / Heidelberg, Germany, pp. 54-65.

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    Abstract

    We are interested in using Inductive Logic Programming(ILP) to infer grammars representing sets of protein sequences. ILP takes as input both examples and background knowledge predicates. This work is a first step in optimising the choice of background knowledge predicates for predicting the function of proteins. We propose methods to obtain different sets of background knowledge. We then study the impact of these sets on inference results through a hard protein function inference task: the prediction of the coupling preference of GPCR proteins. All but one of the proposed sets of background knowledge are statistically shown to have positive impacts on the predictive power of inferred rules, either directly or through interactions with other sets. In addition, this work provides further confirmation, after the work of Muggleton et al. (2001) that ILP can help to predict protein functions.

    Item Type: Book Section
    Editors: Fürnkranz, J, Scheffer, T and Spiliopoulou, M
    Additional Information: Paper originally presented at the 17th European Conference on Machine Learning in Berlin, Germany, September 18-22 2006
    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: Springer
    Refereed: Yes
    Series Name: Lecture notes in artificial intelligence (subseries of Lecture notes in computer science)
    ISBN: 9783540453758
    Related URLs:
    Depositing User: Dr Chris H. Bryant
    Date Deposited: 16 Feb 2009 11:55
    Last Modified: 20 Aug 2013 16:55
    URI: http://usir.salford.ac.uk/id/eprint/1756

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