Predicting human miRNA target genes using a novel evolutionary methodology

Authors:
Korfiati Aigli;Kleftogiannis Dimitris;Theofilatos Konstantinos;Likothanassis Spiros;Tsakalidis Athanasios;Mavroudi Seferina
Affiliations:
Department of Computer Engineering and Informatics, University of Patras, Greece;-;Department of Computer Engineering and Informatics, University of Patras, Greece;Department of Computer Engineering and Informatics, University of Patras, Greece;Department of Computer Engineering and Informatics, University of Patras, Greece;Department of Computer Engineering and Informatics, University of Patras, Greece
Venue:
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Year:
2012

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Abstract

The discovery of miRNAs had great impacts on traditional biology. Typically, miRNAs have the potential to bind to the 3'untraslated region (UTR) of their mRNA target genes for cleavage or translational repression. The experimental identification of their targets has many drawbacks including cost, time and low specificity and these are the reasons why many computational approaches have been developed so far. However, existing computational approaches do not include any advanced feature selection technique and they are facing problems concerning their classification performance and their interpretability. In the present paper, we propose a novel hybrid methodology which combines genetic algorithms and support vector machines in order to locate the optimal feature subset while achieving high classification performance. The proposed methodology was compared with two of the most promising existing methodologies in the problem of predicting human miRNA targets. Our approach outperforms existing methodologies in terms of classification performances while selecting a much smaller feature subset.