Learning Chomsky-like grammars for biological sequence families

Muggleton, SH, Bryant, CH ORCID: https://orcid.org/0000-0002-9002-8343 and Srinivasan, A 2000, 'Learning Chomsky-like grammars for biological sequence families' , in: Proceedings of the17th International Conference on Machine Learning , Morgan Kaufmann, San Francisco, CA, pp. 631-638.

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This paper presents a new method of measuring performance when positives are rare and investigates whether Chomsky-like grammar representations are useful for learning accurate comprehensible predictors of members of biological sequence families. The positive-only learning framework of the Inductive Logic Programming (ILP) system CProgol is used to generate a grammar for recognising a class of proteins known as human neuropeptide precursors (NPPs). As far as these authors are aware, this is both the first biological grammar learnt using ILP and the first real-world scientific application of the positive-only learning framework of CProgol. Performance is measured using both predictive accuracy and a new cost function, em Relative Advantage (RA). The RA results show that searching for NPPs by using our best NPP predictor as a filter is more than 100 times more efficient than randomly selecting proteins for synthesis and testing them for biological activity. The highest RA was achieved by a model which includes grammar-derived features. This RA is significantly higher than the best RA achieved without the use of the grammar-derived features.

Item Type: Book Section
Editors: Langley, P
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: Schools > School of Computing, Science and Engineering
Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Publisher: Morgan Kaufmann
Refereed: Yes
ISBN: 1-55860-707-2
Related URLs:
Depositing User: Dr Chris H. Bryant
Date Deposited: 16 Feb 2009 16:05
Last Modified: 16 Feb 2022 08:16
URI: https://usir.salford.ac.uk/id/eprint/1763

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