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Prediction of topological contacts in proteins using learning classifier systems

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Abstract

Evolutionary based data mining techniques are increasingly applied to problems in the bioinformatics domain. We investigate an important aspect of predicting the folded 3D structure of proteins from their unfolded residue sequence using evolutionary based machine learning techniques. Our approach is to predict specific features of residues in folded protein chains, in particular features derived from the Delaunay tessellations, Gabriel graphs and relative neighborhood graphs as well as minimum spanning trees. Several standard machine learning algorithms were compared to a state-of-the-art learning method, a learning classifier system (LCS), that is capable of generating compact and interpretable rule sets. Predictions were performed for various degrees of precision using a range of experimental parameters. Examples of the rules obtained are presented. The LCS produces results with good predictive performance and generates competent yet simple and interpretable classification rules.

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Authors and Affiliations

  1. Automated Scheduling, Optimization and Planning Research Group, School of Computer Science and IT, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK

    Michael Stout, Jaume Bacardit & Natalio Krasnogor

  2. School of Chemistry, University of Nottingham, University Park, Nottingham, NG7 2RD, UK

    Jonathan D. Hirst

  3. Intelligent Systems Group, Computer Science Department, University College London, Gower Street, London, WC1E 6BT, UK

    Robert E. Smith

Authors
  1. Michael Stout
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  2. Jaume Bacardit
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  3. Jonathan D. Hirst
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  4. Robert E. Smith
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  5. Natalio Krasnogor
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Correspondence to Natalio Krasnogor.

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Stout, M., Bacardit, J., Hirst, J.D. et al. Prediction of topological contacts in proteins using learning classifier systems. Soft Comput 13, 245–258 (2009). https://doi.org/10.1007/s00500-008-0318-8

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