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

  • Yeon-Jin Cho
  • Hyeoncheol Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3683)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yeon-Jin Cho
    • 1
  • Hyeoncheol Kim
    • 1
  1. 1.Department of Computer Science EducationKorea UniversitySeoulKorea

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