Abstract
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. © Springer-Verlag Berlin Heidelberg 2006.
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CITATION STYLE
Kim, H., Yoon, T. S., Zhang, Y., Dikshit, A., & Chen, S. S. (2006). Predictability of rules in HIV-1 protease cleavage site analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3992 LNCS-II, pp. 830–837). Springer Verlag. https://doi.org/10.1007/11758525_111
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