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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Abstract

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:	Author
Depositing User:	Dr Chris H. Bryant
Date Deposited:	16 Feb 2009 16:05
Last Modified:	19 Feb 2019 14:07
URI:	http://usir.salford.ac.uk/id/eprint/1763

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