Chungfan Kim (email@example.com)
Akihiko Konagaya (firstname.lastname@example.org)
Kiyoshi Aasai (email@example.com)
 Japan Advanced Institute of Science and Technology
1-1 Aasahidai, Tatsunokuchi, Ishikawa 923-1292, Japan
 Electrotechnical Laboratories
1-1-4 Umezono, Tsukuba 305-8568,Japan
In this paper, we evaluated the complexity and accuracy of dicodon model for gene finding using Hidden Markov Model with Self-Identification Learning. We used five different models as competitots with smaller parametric space than the dicodon model. Our evaluation result shows that the dicodon model outperforms other competitors in terms of sensitivity as well as specificity. This result indicates that the dicodon model can not be represented by a combination of the pair amino-acid, the codon usage, and the G+C content.