Abstract
In this paper, we demonstrate that the machine learning approach of rule extraction from a trained neural network can be successfully applied to SARS-coronavirus cleavage site analysis. The extracted rules predict cleavage sites better than consensus patterns. Empirical experiments are also shown.
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Keywords
- Neural Network
- Severe Acute Respiratory Syndrome
- Feedforward Neural Network
- Severe Acute Respiratory Syndrome
- Rule Extraction
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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Cho, YJ., Kim, H. (2005). Cleavage Site Analysis Using Rule Extraction from Neural Networks. 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_132
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DOI: https://doi.org/10.1007/11539087_132
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
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