TY - JOUR
T1 - Prediction rule generation of MHC class I binding peptides using ANN and GA
AU - Cho, Yeon Jin
AU - Kim, Hyeoncheol
AU - Oh, Heung Bum
PY - 2005
Y1 - 2005
N2 - A new method is proposed for generating if-then rules to predict peptide binding to class I MHC proteins, from the amino acid sequence of any protein with known binders and non-binders. In this paper, we present an approach based on artificial neural networks (ANN) and knowledge-based genetic algorithm (KBGA) to predict the binding of peptides to MHC class I molecules. Our method includes rule extraction from a trained neural network and then enhancing the extracted rules by genetic evolution. Experimental results show that the method could generate new rules for MHC class I binding peptides prediction.
AB - A new method is proposed for generating if-then rules to predict peptide binding to class I MHC proteins, from the amino acid sequence of any protein with known binders and non-binders. In this paper, we present an approach based on artificial neural networks (ANN) and knowledge-based genetic algorithm (KBGA) to predict the binding of peptides to MHC class I molecules. Our method includes rule extraction from a trained neural network and then enhancing the extracted rules by genetic evolution. Experimental results show that the method could generate new rules for MHC class I binding peptides prediction.
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U2 - 10.1007/11539087_133
DO - 10.1007/11539087_133
M3 - Conference article
AN - SCOPUS:26844482649
VL - 3610
SP - 1009
EP - 1016
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 -