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.
| Original language | English |
|---|---|
| Pages (from-to) | 1009-1016 |
| Number of pages | 8 |
| Journal | Lecture Notes in Computer Science |
| Volume | 3610 |
| Issue number | PART I |
| DOIs | |
| Publication status | Published - 2005 |
| Event | First International Conference on Natural Computation, ICNC 2005 - Changsha, China Duration: 2005 Aug 27 → 2005 Aug 29 |
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
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Cho, Y. J., Kim, H., & Oh, H. B. (2005). Prediction rule generation of MHC class I binding peptides using ANN and GA. Lecture Notes in Computer Science, 3610(PART I), 1009-1016. https://doi.org/10.1007/11539087_133