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

Authors:
Yeon-Jin Cho;Hyeoncheol Kim
Affiliations:
Department of Computer Science Education, Korea University, Seoul, Korea;Department of Computer Science Education, Korea University, Seoul, Korea
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Year:
2005

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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.