An association rule based approach for biological sequence feature classification

Authors:
David Becerra;Diana Vanegas;Giovanni Cantor;Luis Niño
Affiliations:
Intelligent Systems Research Laboratory, National University of Colombia;Intelligent Systems Research Laboratory, National University of Colombia;Intelligent Systems Research Laboratory, National University of Colombia;Intelligent Systems Research Laboratory, National University of Colombia
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

Citing 5
Cited 0

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A new representation for protein secondary structure prediction based on frequent patterns

Bioinformatics

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Abstract

In this paper, an extraction and classification feature approach of biological sequences based on profiles built using an association analysis is proposed. The most important features of the approach are: i) The use of data mining techniques to perform knowledge extraction from biological sequences. Specifically an association analysis process is proposed as a methodology for discovering interesting relationships hidden in biological data sets; and ii) Some learning classifiers are proposed to be trained using binary profiles obtained from the association analysis process. These learning methods were applied over a sequence structure layer of secondary structure predictors to analyze the performance of association rules as a pattern extraction method. Some experiments were carried out to validate the proposed approach obtaining very promising results.