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 |
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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: | 19 Feb 2019 14:07 |
URI: | http://usir.salford.ac.uk/id/eprint/1763 |
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