Predicting human miRNA target genes using a novel evolutionary methodology
Kleftogiannis, Dimitrios A.
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
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AbstractThe 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. © 2012 Springer-Verlag.
PublisherSpringer Science + Business Media
Conference/Event name7th Hellenic Conference on Artificial Intelligence, SETN 2012