Recognition of transcription factor binding sites (TFBSs or DNA motifs) to help with understanding the regulation of gene expression is one of the major challenges in the post-genomics era. Computational approaches have been developed to perform binding sites discovery based on brute-force search techniques or heuristic search algorithms, and numbers of them have achieved some degrees of success. However, the prediction accuracy of the algorithm can be relatively influenced by the natural low signal-to-noise ratio of the DNA sequence. In this paper, a novel DNA motif discovery approach using a genetic algorithm is proposed to explore the ways to improve the algorithm performance. We take account of the publicly available motif models such as Position Frequency Matrix (PFM) to initialize the population. By considering both conservation and complexity of the DNA motifs, a novel fitness function is developed to better evaluate the motif models during the evolution process. A final model refinement process is also introduced for optimizing the motif models. The experimental results demonstrate a comparable (superior) performance of our approach to recently proposed two genetic algorithm motif discovery approaches.
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