Abstract
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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Cho, YJ., Kim, H., Oh, HB. (2005). Prediction Rule Generation of MHC Class I Binding Peptides Using ANN and GA. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_133
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DOI: https://doi.org/10.1007/11539087_133
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
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