Multi objective particle swarm optimization approach for DNA sequence design
Finding reliable and efficient DNA sequences is one of the most important tasks for successful DNA related experiments such as DNA computing. In DNA computing, perfect hybridization between a sequence and its base-pairing complement is important to retrieve the information stored in the sequences an...
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| Format: | Thesis |
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2010
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| Summary: | Finding reliable and efficient DNA sequences is one of the most important tasks for successful DNA related experiments such as DNA computing. In DNA computing, perfect hybridization between a sequence and its base-pairing complement is important to retrieve the information stored in the sequences and to operate the computation processes. For this reason, the desired set of good DNA sequences, which have a stable duplex with their complement, are highly required. Various kinds of methods and strategies have been proposed to date to obtain good DNA sequences designed sequences using overlapping subsequences to enforce uniqueness. In this study a multi objective PSO is implemented to design DNA sequences based on Pareto optimality non-dominated solutions. The ability of the proposed approach is to detect the true Pareto optimal solutions and capture the true Pareto optimal front. By this method four objective functions, namely Hmeasure and similarity, hairpin, continuity were minimized simultaneously. The results were obtained from multi objective particle swarm optimization based on non dominated solutions are unique sequences which cannot be hybridized with other sequences in the set. |
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