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.
Chapter PDF
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)
Benson, D.A., Karsch-Mizrachi, I., Lipman, D.J., Ostell, J., Wheeler, D.L.: GenBank: update. Nucleic Acids Res., 32 Database issue, D23–D26 (2004)
Blom, N., Hansen, J., Blaas, D., Brunak, S.: Cleavage site analysis in picornaviral polyproteins: discovering cellular targets by neural networks. Protein Sci. 5, 2203–2216 (1996)
Chen, L.L., Ou, H.Y., Zhang, R., Zhang, C.T.: ZCURVE-CoV: a new system to recognize protein coding genes in coronavirus genomes, and its applications in analyzing SARS-CoV genomes. SCIENCE DIRECT, BBRC, 382–388 (2003)
De Jong, K.A., Spears, W.M.: Learning Concept Classification Rules Using Genetic Algorithms. In: Proceedings of the I Zth. international Conference on Artificial Intelligence, pp. 651–656 (1991)
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)
Fu, L.: Introduction to knowledge-based neural networks. Knowledge-Based Systems 8(6), 299–300 (1995)
Fu, L., Kim, H.: Abstraction and Representation of Hidden Knowledge in an Adapted Neural Network. unpublished, CISE, University of Florida (1994)
Gaoa, F., Oua, H.Y., Chena, L.L., Zhenga, W.X., Zhanga, C.T.: Prediction of proteinase cleavage sites in polyproteins of coronaviruses and its applications in analyzing SARS-CoV genomes. FEBS Letters 553, 451–456 (2003)
Kiemer, L., Lund, O., Brunak, S., Blom, N.: Coronavirus 3CL-pro proteinase cleavage sites: Possible relevance to SARS virus pathology. BMC Bioinformatics (2004)
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)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, New York (1996)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)
Narayanan, A., Wu, X., Yang, Z.R.: Mining viral protease data to extract cleavage knowledge. Bioinformatics 18(1), S5–S13 (2002)
Setino, R., Liu, H.: Understanding neural networks via rule extraction. In: Proceedings of the 14th International Conference on Neural Networks, Montreal, Canada, vol. (1), pp. 480–485 (1995)
Stadler, K., Masignani, V., Eickmann, M., Becker, S., Abrignani, S., Klenk, H.D., Rappuoli, R.: Sars - Beginning To Understand A New Virus. Nature Reviews, Microbiology 1, 209–218 (2003)
Taha, I.A., Ghosh, J.: Symbolic interpretation of artificial neural networks. IEEE Transactions on Knowledge and Data Engineering 11(3), 443–463 (1999)
Towell, G.G., Shavlik, J.W.: Extracting refined rules from knowledgebased neural networks. Machine Learning 13(1) (1993)
Yap, Y.L., Zhang, X.W., Danchin, A.: Relationship of SARS-CoV to other pathogenic RNA viruses explored by tetranucleotide usage profiling. BMC Bioinformatics (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cho, YJ., Kim, H. (2005). Rule Generation Using NN and GA for SARS-CoV Cleavage Site Prediction. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553939_111
Download citation
DOI: https://doi.org/10.1007/11553939_111
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28896-1
Online ISBN: 978-3-540-31990-0
eBook Packages: Computer ScienceComputer Science (R0)