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Knowledge-Based Intelligent Information and Engineering Systems

Volume 3683 of the series Lecture Notes in Computer Science pp 785-791

Rule Generation Using NN and GA for SARS-CoV Cleavage Site Prediction

  • Yeon-Jin ChoAffiliated withDepartment of Computer Science Education, Korea University
  • , Hyeoncheol KimAffiliated withDepartment of Computer Science Education, Korea University

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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.