Abstract
Kernel self-organizing map has been recently studied by Fyfe and his colleagues [1]. This paper investigates the use of a novel bio-kernel function for the kernel self-organizing map. For verification, the application of the proposed new kernel self-organizing map to HIV drug resistance classification using mutation patterns in protease sequences is presented. The original self-organizing map together with the distributed encoding method was compared. It has been found that the use of the kernel self-organizing map with the novel bio-kernel function leads to better classification and faster convergence rate ...
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Keywords
- Protease Cleavage Site
- Support Vector Machine Approach
- Signal Peptide Cleavage Site
- Regularization Factor
- Protein Secondary Structure Prediction
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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Yang, Z.R., Young, N. (2005). Bio-kernel Self-organizing Map for HIV Drug Resistance Classification. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_20
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DOI: https://doi.org/10.1007/11539087_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
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