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Title Prediction of MHC class II epitopes using genetic algorithms and other metaheuristics
Author Mygind, Henrik Egeberg
Mølgaard, Morten
Supervisor Fischer, Paul (Algorithms and Logic, Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
Institution Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark
Thesis level Bachelor thesis
Year 2009
Abstract 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 interpreted from a computer science perspective. The genetic algorithm has been implemented in Java. The results have been thoroughly analysed and compared with the Gibbs sampler using the statistical tool R. The genetic algorithm has proven more effective at optimising the objective function than the Gibbs sampler. This improvement in optimisation has not entailed a better prediction. This leads to the conclusion that to get a better prediction a better objective function has to be found.
Series IMM-B.Sc.-2009-11
Fulltext
Original PDF bac09_11_net.pdf (3.40 MB)
Admin Creation date: 2009-07-02    Update date: 2010-10-28    Source: dtu    ID: 246134    Original MXD