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Using mRNA secondary structure predictions improves recognition of known yeast functional uORFs

Selpi, Selpi, Bryant, CH and Kemp, GJL 2008, 'Using mRNA secondary structure predictions improves recognition of known yeast functional uORFs' , in: Proceedings of 2nd International Workshop on Machine Learning in Systems Biology , University of Liege , pp. 85-94.

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    Abstract

    We are interested in using inductive logic programming ILP)to generate rules for recognising functional upstream open reading frames (uORFs) in the yeast Saccharomyces cerevisiae. This paper empirically investigates whether providing an ILP system with predicted mRNA secondary structure can increase the performance of the resulting rules. Two sets of experiments, with and without mRNA secondary structure predictions as part of the background knowledge, were run. For each set, stratified 10-fold cross-validation experiments were run 100 times, each time randomly permuting the order of the positive training examples, and the performance of the resulting hypotheses were measured. Our results demonstrate that the performance of an ILP system in recognising known functional uORFs in the yeast S.cerevisiae significantly increases when mRNA secondary structure predictions are added to the background knowledge and suggest that mRNA secondary structure can affect the ability of uORFs to regulate gene expression.

    Item Type: Book Section
    Editors: Wehenkel, L., Geurts, P., Moreau, Y. and d'Alche-Buc, F
    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: University of Liege
    Refereed: Yes
    Related URLs:
    Depositing User: Dr Chris H. Bryant
    Date Deposited: 16 Feb 2009 13:34
    Last Modified: 20 Aug 2013 16:55
    References:
    URI: http://usir.salford.ac.uk/id/eprint/1752

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