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.
Keywords
- 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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References
Andrews, R., Diederich, J., Tickle, A.B.: Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems 8(6), 373–389 (1995)
Cai, Y.-D., Chou, K.-C.: Artificial neural network model for predicting HIV protease cleavage sites in protein. Advances in Engineering Software 29, 119–128 (1998)
Cai, Y.-D., Liu, X.J., Xu, X.B., Chou, K.-C.: Support vector machines for predicting hiv protease cleavage sites in protein. Journal of Computational Chemistry 23, 267–274 (2002)
Fu, L.: Neural Networks in Computer Intelligence. McGraw Hill, Inc., New York (1994)
Fu, L.: Rule generation from neural networks. IEEE Transactions on Systems, Man, and Cybernetics 24(8), 1114–1124 (1994)
Kim, H.: Computationally efficient heuristics for if-then rule extraction from feed-forward neural networks. In: Morishita, S., Arikawa, S. (eds.) DS 2000. LNCS (LNAI), vol. 1967, pp. 170–182. Springer, Heidelberg (2000)
Lumini, A., Loris, N.: Machine learning for hiv-1 protease cleavage site prediction. In: Proceedings of Artificial Intelligence and Application (AIA 2005) (2005)
Narayanan, A., Wu, X., Yang, Z.R.: Mining viral protease data to extract cleavage knowledge. Bioinformatics 18(Suppl.1), S5–S13 (2002)
Rögnvaldsson, T., You, L.: Why neural networks should not be used for HIV-1 protease cleavage site prediction. Bioinformatics 20(11), 1702–1709 (2004)
Setino, R., Liu, H.: Understanding neural networks via rule extraction. In: Proceedings of the 14th International Conference on Neural Networks, vol. (1), pp. 480–485 (1995)
Taha, I.A., Ghosh, J.: Symbolic interpretation of artificial neural networks. IEEE Transactions on Knowledge and Data Engineering 11(3), 443–463 (1999)
You, L., Garwicz, D., Rögnvaldsson, T.: Comprehensive bioinformatic analysis of the specificity of human immunodeficiency virus type 1 protease. Journal of Virology 79(19), 12477–12486 (2005)
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© 2006 Springer-Verlag Berlin Heidelberg
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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
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