A Novel Data Mining Approach for the Accurate Prediction of Translation Initiation Sites

G. Tzanis, C. Berberidis, I. Vlahavas, “A Novel Data Mining Approach for the Accurate Prediction of Translation Initiation Sites”, 7th International Symposium on Biological and Medical Data Analysis, Nicos Maglaveras et al. (Ed.), Springer-Verlag, pp. 92-103, Thessaloniki, Greece, 2006.

7th International Symposium on Biological and Medical Data Analysis, Nicos Maglaveras et al. (Ed.), Springer-Verlag, pp. 92-103, Thessaloniki, Greece, 2006.

In an mRNA sequence, the prediction of the exact codon where the process of translation starts (Translation Initiation Site – TIS) is a particularly important problem. So far it has been tackled by several researchers that apply various statistical and machine learning techniques, achieving high accuracy levels, often over 90%. In this paper we propose a mahine learning approach that can further improve the prediction accuracy. First, we provide a concise review of the literature in this field. Then we propose a novel feature set. We perform extensive experiments on a publicly available, real world dataset for various vertebrate organisms using a variety of novel features and classification setups. We evaluate our results and compare them with a reference study and show that our approach that involves new features and a combination of the Ribosome Scanning Model with a meta-classifier shows higher accuracy in most cases.