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

Yeon J. Cho, Hyeoncheol Kim

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages785-791
Number of pages7
Volume3683 LNAI
Publication statusPublished - 2005 Dec 1
Event9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 - Melbourne, Australia
Duration: 2005 Sep 142005 Sep 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3683 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005
CountryAustralia
CityMelbourne
Period05/9/1405/9/16

Fingerprint

SARS Virus
Severe Acute Respiratory Syndrome
Rule Generation
Prediction
Rule Extraction
Molecular biology
Molecular Evolution
Molecular Biology
Protease
Peptide Hydrolases
Neural Networks
Neural networks
Experimental Results
Gas

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Cho, Y. J., & Kim, H. (2005). Rule generation using NN and GA for SARS-CoV cleavage site prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3683 LNAI, pp. 785-791). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3683 LNAI).

Rule generation using NN and GA for SARS-CoV cleavage site prediction. / Cho, Yeon J.; Kim, Hyeoncheol.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3683 LNAI 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 proceedingConference contribution

Cho, YJ & Kim, H 2005, Rule generation using NN and GA for SARS-CoV cleavage site prediction. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3683 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3683 LNAI, pp. 785-791, 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005, Melbourne, Australia, 05/9/14.
Cho YJ, Kim H. Rule generation using NN and GA for SARS-CoV cleavage site prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3683 LNAI. 2005. p. 785-791. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Cho, Yeon J. ; Kim, Hyeoncheol. / Rule generation using NN and GA for SARS-CoV cleavage site prediction. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3683 LNAI 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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