Abstract
The study and comparison of sequences of characters from a finite alphabet is relevant to various areas of science, notably molecular biology. The measurement of sequence similarity involves the consideration of the possible sequence alignments in order to find an optimal one for which the “distance” between sequences is minimum. In biology informatics area, it is a more important and difficult problem due to the long length (100 at least) of sequence, this cause the compute complexity and large memory require. By associating a path in a lattice to each alignment, a geometric insight can be brought into the problem of finding an optimal alignment, this give an obvious encoding of each path. This problem can be solved by applying genetic algorithm, which is more efficient than dynamic programming and hidden Markov model using commomly now.
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Foundation item: Supported by Zi-qiang Foundation of Wuhan University and Open Foundation of the State Key-Laboratory of Software Engineering, Wuhan University
Biography: Shi Feng(1966-), male, Associate professor, research direction: bioinformatics.
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Feng, S., Jing, H., Zhong-xi, M. et al. A genetic algorithm on multiple sequences alignment problems in biology. Wuhan Univ. J. Nat. Sci. 7, 139–144 (2002). https://doi.org/10.1007/BF02830301
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DOI: https://doi.org/10.1007/BF02830301