Top-Down Motif Discovery in Biological Sequence Datasets by Genetic Algorithm

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.