Predictability of Rules in HIV-1 Protease Cleavage Site Analysis

  • Hyeoncheol Kim
  • Tae-Sun Yoon
  • Yiying Zhang
  • Anupam Dikshit
  • Su-Shing Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3992)


Symbolic rules play an important role in HIV-1 protease cleavage site prediction. Recently, some studies have done on extraction of the prediction rules with some success. In this paper, we demonstrated a decompositional approach for rule extraction from nonlinear neural networks. We also compared the prediction rules to the ones extracted by other approaches and methods. Empirical experiments are also shown.


Prediction Rule Association Rule Mining Decompositional Approach Rule Extraction Trained Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hyeoncheol Kim
    • 1
  • Tae-Sun Yoon
    • 1
  • Yiying Zhang
    • 2
  • Anupam Dikshit
    • 2
  • Su-Shing Chen
    • 2
  1. 1.Dept. of Computer Science EducationKorea UniversitySeoulKorea
  2. 2.Computer & Info. Sciences and EngineeringUniversity of FloridaGainesvilleU.S.A.

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