Predicting human miRNA target genes using a novel evolutionary methodology

Korfiati Aigli, Dimitrios A. Kleftogiannis, Theofilatos Konstantinos, Likothanassis Spiros, Tsakalidis Athanasios, Mavroudi Seferina

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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. © 2012 Springer-Verlag.
Original languageEnglish (US)
Title of host publicationArtificial Intelligence: Theories and Applications
PublisherSpringer Nature
Pages291-298
Number of pages8
ISBN (Print)9783642304477
DOIs
StatePublished - 2012

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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