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Are grammatical representations useful for learning from biological sequence data?--a case study.

S H Muggleton1, C H Bryant, A Srinivasan

  • 1Department of Computer Science, University of York, York YO10 5DD, United Kingdom.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|November 6, 2001
PubMed
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This study demonstrates that Chomsky-like grammar representations, learned using Inductive Logic Programming (ILP), significantly improve the efficiency of identifying human neuropeptide precursors (NPPs). This bioinformatics approach offers over 100x greater efficiency than random methods.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Existing bioinformatics methods lack cost-efficiency for identifying novel human neuropeptide precursors (NPPs).
  • Hand-coding a grammar for NPP recognition by specialists proved unsuccessful.
  • A need exists for improved, cost-effective methods in biological sequence family analysis.

Purpose of the Study:

  • To investigate the utility of Chomsky-like grammar representations for learning cost-effective biological sequence predictors.
  • To develop a novel method for recognizing human neuropeptide precursors (NPPs) using Inductive Logic Programming (ILP).
  • To enhance the efficiency of discovering new NPPs.

Main Methods:

  • Utilized the Inductive Logic Programming (ILP) Bayesian approach for learning from positive examples to generate a biological grammar.

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  • Derived features from the ILP-generated grammar and other learning strategies.
  • Developed recognition models using C4.5 and C4.5rules, evaluating performance with predictive accuracy and a novel cost function, Relative Advantage (RA).

    • Main Results:

    • The best predictor, incorporating grammar-derived features, achieved over 100 times greater efficiency in searching for novel NPPs compared to random selection.
    • Models including grammar-derived features demonstrated significantly higher Relative Advantage (RA) than those without.
    • Predictive accuracy was found to be an inadequate performance measure for this specific domain due to class imbalance.

    Conclusions:

    • Chomsky-like grammar representations are effective for learning cost-effective predictors in biological sequence families.
    • The ILP Bayesian approach represents a novel and successful application for biological grammar induction.
    • Grammar-derived features significantly enhance the performance and cost-effectiveness of NPP recognition models.