Rule generation using NN and GA for SARS-CoV cleavage site prediction

Yeon Jin Cho; Hyeoncheol Kim

doi:10.1007/11553939_111

Rule generation using NN and GA for SARS-CoV cleavage site prediction

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Cleavage site prediction is an important issue in molecular biology. We present a new method that generates prediction rules for SARS-CoV protease cleavage sites. Our method includes rule extraction from a trained neural network and then enhancing the extracted rules by genetic evolution to improve its quality. Experimental results show that the method could generate new rules for cleavage site prediction, which are more general and accurate than consensus patterns.

Original language	English
Title of host publication	Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings
Publisher	Springer Verlag
Pages	785-791
Number of pages	7
ISBN (Print)	3540288961, 9783540288961
DOIs	https://doi.org/10.1007/11553939_111
Publication status	Published - 2005
Event	9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 - Melbourne, Australia Duration: 2005 Sept 14 → 2005 Sept 16

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3683 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005
Country/Territory	Australia
City	Melbourne
Period	05/9/14 → 05/9/16

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/11553939_111

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Cho, Y. J., & Kim, H. (2005). Rule generation using NN and GA for SARS-CoV cleavage site prediction. In Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings (pp. 785-791). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3683 LNAI). Springer Verlag. https://doi.org/10.1007/11553939_111

Rule generation using NN and GA for SARS-CoV cleavage site prediction. / Cho, Yeon Jin; Kim, Hyeoncheol.
Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Springer Verlag, 2005. p. 785-791 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3683 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Cho, YJ & Kim, H 2005, Rule generation using NN and GA for SARS-CoV cleavage site prediction. in Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3683 LNAI, Springer Verlag, pp. 785-791, 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005, Melbourne, Australia, 05/9/14. https://doi.org/10.1007/11553939_111

Cho YJ, Kim H. Rule generation using NN and GA for SARS-CoV cleavage site prediction. In Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Springer Verlag. 2005. p. 785-791. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11553939_111

Cho, Yeon Jin ; Kim, Hyeoncheol. / Rule generation using NN and GA for SARS-CoV cleavage site prediction. Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. Springer Verlag, 2005. pp. 785-791 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Rule generation using NN and GA for SARS-CoV cleavage site prediction

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