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Author(s): G. Tzanis, I. Kavakiotis, I. Vlahavas.
Title: “PolyA-iEP: A Data Mining Method for the Effective Prediction of Polyadenylation Sites”.
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Keywords:
data mining, machine learning, classification, emerging pattern, bioinformatics, polyadenylation.
Appeared in:
Expert Systems with Applications, Elsevier, 38(10), pp. 1239812408, 2011. Abstract: This paper presents a study on polyadenylation site prediction, which is a very important problem in bioinformatics and medicine, promising to give a lot of answers especially in cancer research. We describe a method, called PolyA-iEP, that we developed for predicting polyadenylation sites and we present a systematic study of the problem of recognizing mRNA 3΄ ends which contain a polyadenylation site using the proposed method. PolyA-iEP is a modular system consisting of two main components that both contribute substantially to the descriptive and predictive potential of the system. In specific, PolyA-iEP exploits the advantages of emerging patterns, namely high understandability and discriminating power and the strength of a distance-based scoring method that we propose. The extracted emerging patterns may span across many elements around the polyadenylation site and can provide novel and interesting biological insights. The outputs of these two components are finally combined by a classifier in a highly effective framework, which in our setup reaches 93.7% of sensitivity and 88.2% of specificity. PolyA-iEP can be parameterized and used for both descriptive and predictive analysis. We have experimented with Arabidopsis thaliana sequences for evaluating our method and we have drawn important conclusions.
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