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.
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Kim, H., Yoon, TS., Zhang, Y., Dikshit, A., Chen, SS. (2006). Predictability of Rules in HIV-1 Protease Cleavage Site Analysis. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3992. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11758525_111
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DOI: https://doi.org/10.1007/11758525_111
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34381-3
Online ISBN: 978-3-540-34382-0
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