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 |
|---|---|
| Title of host publication | Lecture Notes in Computer Science |
| Editors | L. Wang, K. Chen, Y.S. Ong |
| Pages | 1002-1008 |
| Number of pages | 7 |
| Volume | 3610 |
| Edition | PART I |
| Publication status | Published - 2005 |
| Event | First International Conference on Natural Computation, ICNC 2005 - Changsha, China Duration: 2005 Aug 27 → 2005 Aug 29 |
Other
| Other | First International Conference on Natural Computation, ICNC 2005 |
|---|---|
| Country | China |
| City | Changsha |
| Period | 05/8/27 → 05/8/29 |
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ASJC Scopus subject areas
- Computer Science (miscellaneous)
Cite this
Cleavage site analysis using rule extraction from neural networks. / Cho, Yeun Jin; Kim, Hyeoncheol.
Lecture Notes in Computer Science. ed. / L. Wang; K. Chen; Y.S. Ong. Vol. 3610 PART I. ed. 2005. p. 1002-1008.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Cleavage site analysis using rule extraction from neural networks
AU - Cho, Yeun Jin
AU - Kim, Hyeoncheol
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=26844465130&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=26844465130&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:26844465130
VL - 3610
SP - 1002
EP - 1008
BT - Lecture Notes in Computer Science
A2 - Wang, L.
A2 - Chen, K.
A2 - Ong, Y.S.
ER -