Rule Generation Using NN and GA for SARS-CoV Cleavage Site Prediction
Identifieur interne : 001010 ( Main/Exploration ); précédent : 001009; suivant : 001011Rule Generation Using NN and GA for SARS-CoV Cleavage Site Prediction
Auteurs : Yeon-Jin Cho [Corée du Sud] ; Hyeoncheol Kim [Corée du Sud]Source :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
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.
Url:
DOI: 10.1007/11553939_111
Affiliations:
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