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Translation initiation sites (TISs) are important signals in cDNA sequences. Many research efforts have tried to predict TISs in cDNA sequences. In this paper, we propose to use mixture Gaussian models for TIS prediction. Using both local features and some features generated from global measures, the proposed method predicts TISs with a sensitivity of 98 percent and a specificity of 93.6 percent. Our method outperforms many other existing methods in sensitivity while keeping specificity high. We attribute the improvement in sensitivity to the nature of the global features and the mixture Gaussian models.
Index Terms- Bioinformatics, classification, feature extraction, mixture Gaussian model, translation initiation sites.

G. Li, L. Zhang and T. Leong, "Translation Initiation Sites Prediction with Mixture Gaussian Models in Human cDNA Sequences," in IEEE Transactions on Knowledge & Data Engineering, vol. 17, no. , pp. 1152-1160, 2005.
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