Mining an optimal prototype from a periodic time series: an evolutionary computation-based approach
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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This paper presents a novel approach for motif discovery. Finding motif in biosequences is the most important primitive operation in computational biology. There are many computational requirements for a motif discovery algorithm such as computer memory space requirement and computational complexity. To overcome the complexity of motif discovery, we propose an alternative solution integrating genetic algorithm and top-down data mining approaches for eliminating multiple sequence alignment process. The experimental results demonstrate that the proposed method outperforms two well-known motif discovery algorithms, called MEME and Gibbs Sampler.