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A Hybrid Rule-Induction/Likelihood-Ratio Based Approach for Predicting Protein-Protein Interactions

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Computational Intelligence

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 1))

Abstract

We propose a new hybrid data mining method for predicting protein-protein interactions combining Likelihood-Ratio with rule induction algorithms. In essence, the new method consists of using a rule induction algorithm to discover rules representing partitions of the data, and then the discovered rules are interpreted as “bins” which are used to compute likelihood ratios. This new method is applied to the prediction of protein-protein interactions in the Saccharomyces Cerevisiae genome, using predictive genomic features in an integrated scheme. The results show that the new hybrid method outperforms a pure likelihood ratio based approach.

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Authors and Affiliations

  1. Centre for Biomedical Informatics and Computing Laboratory, University of Kent, Canterbury, U.K.

    Mudassar Iqbal, Alex A. Freitas & Colin G. Johnson

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  1. Mudassar Iqbal
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  2. Alex A. Freitas
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  3. Colin G. Johnson
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Editors and Affiliations

  1. School of Computer Science, Cardiff University , 5 The Parade,Roath, CF24 3AA, Cardiff, UK

    Christine L. Mumford

  2. University of South Australia Adelaide , Mawson Lakes Campus, South Australia, Australia

    Lakhmi C. Jain

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Iqbal, M., Freitas, A.A., Johnson, C.G. (2009). A Hybrid Rule-Induction/Likelihood-Ratio Based Approach for Predicting Protein-Protein Interactions. In: Mumford, C.L., Jain, L.C. (eds) Computational Intelligence. Intelligent Systems Reference Library, vol 1. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01799-5_19

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  • DOI: https://doi.org/10.1007/978-3-642-01799-5_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01798-8

  • Online ISBN: 978-3-642-01799-5

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