• Corpus ID: 59972657

Prediction of MHC class II epitopes using genetic algorithms and other metaheuristics

@inproceedings{Mygind2009PredictionOM,
  title={Prediction of MHC class II epitopes using genetic algorithms and other metaheuristics},
  author={H. Mygind and M. M{\o}lgaard},
  year={2009}
}
As a part of immunological bioinformatics research metaheuristics are used in the prediction of amino acid chains binding to the MHC-II molecule. A method of prediction the Gibbs sampler, developed by Nielsen et. al.[8], uses simulated annealing to optimise an objective function. We have replaced the simulated annealing with a genetic algorithm, in an attempt to perform a better optimisation and thereby achieve a better prediction. The bioinformatical problem of MHC-II binding is… 
Major Histocompatibility Complex Class II Prediction
TLDR
Generally, these results indicate that GA has a strong ability for MHC Class II binding prediction, and the genetic algorithmpresented here shows increased prediction accuracy with higher number of true positives and negatives on almost of MHC class II alleles.

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