Learning Chomsky-like grammars for biological sequence families
Muggleton, SH, Bryant, CH 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|
|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 (SIRC)
|Depositing User:||Dr Chris H. Bryant|
|Date Deposited:||16 Feb 2009 16:05|
|Last Modified:||01 Dec 2015 00:04|
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