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)

    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 languageEnglish
    Pages (from-to)1009-1016
    Number of pages8
    JournalLecture Notes in Computer Science
    Volume3610
    Issue numberPART I
    DOIs
    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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