TY - JOUR
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
U2 - 10.1007/11539087_132
DO - 10.1007/11539087_132
M3 - Conference article
AN - SCOPUS:26844465130
VL - 3610
SP - 1002
EP - 1008
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SN - 0302-9743
IS - PART I
T2 - First International Conference on Natural Computation, ICNC 2005
Y2 - 27 August 2005 through 29 August 2005
ER -