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

Yeon J. Cho, Hyeoncheol Kim, Heung B. Oh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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
Title of host publicationLecture Notes in Computer Science
EditorsL. Wang, K. Chen, Y.S. Ong
Pages1009-1016
Number of pages8
Volume3610
EditionPART I
Publication statusPublished - 2005
EventFirst International Conference on Natural Computation, ICNC 2005 - Changsha, China
Duration: 2005 Aug 272005 Aug 29

Other

OtherFirst International Conference on Natural Computation, ICNC 2005
CountryChina
CityChangsha
Period05/8/2705/8/29

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

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  • Cite this

    Cho, Y. J., Kim, H., & Oh, H. B. (2005). Prediction rule generation of MHC class I binding peptides using ANN and GA. In L. Wang, K. Chen, & Y. S. Ong (Eds.), Lecture Notes in Computer Science (PART I ed., Vol. 3610, pp. 1009-1016)