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Identifying peptide motifs using genetic algorithms

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dc.contributor Graduate Program in Computer Engineering.
dc.contributor.advisor Ersoy, Cem.
dc.contributor.advisor Sezerman, Uğur.
dc.contributor.author Tanrıseven, Deniz.
dc.date.accessioned 2023-03-16T10:00:12Z
dc.date.available 2023-03-16T10:00:12Z
dc.date.issued 2000.
dc.identifier.other CMPE 2000 T36
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/12156
dc.description.abstract Finding the ligand motifs binding to the receptor molecules is crucial in vaccine and drug design, especially for the MHC-peptide problem. In this work, for determining the peptide motifs binding to specific MHC molecules, we have used regression analysis. In order to find the the optimum regression line, genetic algorithm (GA) techniques are used because in traditional regression analysis methods, you may not be able to reach the optimum solution. The optimum regression line generated by the GA also determines the factors on the MHC molecules that makes the peptide bind to these MHC molecules. The efficiency of the GA is tested by doing several tests on its different parameters, and the optimum set of parameters are determined for this problem. Results have shown that we are able to predict second position of a peptide motif with 95 per cent exact match or 100 per cent close match within one standard deviation of the predicted equation. We have divided last position's data into two parts in order to explain it with two regression lines. Predictions for the last position of the peptide motif with the first regression line resulted in 80 per cent exact match. Second regression line resulted in 75 per cent exact match.
dc.format.extent 30 cm. +
dc.publisher Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2000.
dc.subject.lcsh Major histocompatibility complex.
dc.subject.lcsh Genetic algorithms.
dc.title Identifying peptide motifs using genetic algorithms
dc.format.pages xiv, 76 leaves ;


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