A novel data mining approach for the accurate prediction of translation initiation sites

Authors:
George Tzanis;Christos Berberidis;Ioannis Vlahavas
Affiliations:
Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece;Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece;Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece
Venue:
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
Year:
2006

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Abstract

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.