A Hybrid Approach for Multiple Sequence Alignment Using Genetic Algorithms and Heuristic Dynamic Programming
碩士 === 樹德科技大學 === 資訊管理研究所 === 91 === Multiple sequence alignment (MSA) is a fundamental problem in the study of computational molecular biology and refers to the search for maximal similarity overall DNA /protein sequences. Unfortunately, existed multiple sequence alignment algorithms work well for...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2003
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| Online Access: | http://ndltd.ncl.edu.tw/handle/37758988061872283394 |
| Summary: | 碩士 === 樹德科技大學 === 資訊管理研究所 === 91 === Multiple sequence alignment (MSA) is a fundamental problem in the study of computational molecular biology and refers to the search for maximal similarity overall DNA /protein sequences. Unfortunately, existed multiple sequence alignment algorithms work well for sequences with high similarity but do not scale well when either the length or the number of the sequences is large. In this paper, we view the multiple sequence alignments problem as an optimization problem and propose a hybrid approach to solve it. The hybrid approach combines genetic algorithm and heuristic dynamic programming method.
Genetic Algorithms (GAs) has been used in a wide variety of applications to find solutions for hard optimization problems. The purpose of the heuristic dynamic programming is used to provide a seed for the initial population used in GAs. We design five kinds of problem-dependent mutation operators and simple realignment rules used in evolution process. The proposed approach has been test on several sets of artificially generated sequences and real biological sequences. The experimental results are compared with a variety of MSA methods to illustrate the effectiveness of the proposed approach.
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