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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Prediction Rule Generation of MHC Class I Binding Peptides Using ANN and GA
verfasst von : Yeon-Jin Cho, Hyeoncheol Kim, Heung-Bum Oh
Erschienen in: Advances in Natural Computation
Verlag: Springer Berlin Heidelberg
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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.