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.
Original language | English |
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Pages (from-to) | 1002-1008 |
Number of pages | 7 |
Journal | Lecture Notes in Computer Science |
Volume | 3610 |
Issue number | PART I |
DOIs | |
Publication status | Published - 2005 |
Event | First International Conference on Natural Computation, ICNC 2005 - Changsha, China Duration: 2005 Aug 27 → 2005 Aug 29 |
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science