Prediction rule generation of MHC class I binding peptides using ANN and GA

Yeon Jin Cho, Hyeoncheol Kim, Heung Bum Oh

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)


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 languageEnglish
Pages (from-to)1009-1016
Number of pages8
JournalLecture Notes in Computer Science
Issue numberPART I
Publication statusPublished - 2005
EventFirst International Conference on Natural Computation, ICNC 2005 - Changsha, China
Duration: 2005 Aug 272005 Aug 29

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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