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Title: Mining Frequent Patterns and Association Rules from Biological Data
Author(s): I. Kavakiotis, G. Tzanis, I. Vlahavas.
Keywords: Data Mining, Machine Learning, Classification, Emerging Patterns, Bioinformatics, Polyadenylation.
Appeared in: Biological Knowledge Discovery Handbook: Preprocessing, Mining and Postprocessing of Biological Data, Mourad Elloumi and Albert Y. Zomaya (Eds.), Wiley-Blackwell (John Wiley & Sons), 2014.
Abstract: This chapter presents a method called PolyA-iEP that has been developed for the prediction of polyadenylation sites. More precisely PolyA-iEP is a method that recognizes mRNA 3’ends which contain polyadenylation sites. It is a modular system which consists of two main components. The first exploits the advantages of emerging patters and the second is a distance-based scoring method. The outputs of the two components are finally combined by a classifier. The final results reach very high scores of sensitivity and specificity.
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