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dc.contributor.authorAigli, Korfiati
dc.contributor.authorKleftogiannis, Dimitrios A.
dc.contributor.authorKonstantinos, Theofilatos
dc.contributor.authorSpiros, Likothanassis
dc.contributor.authorAthanasios, Tsakalidis
dc.contributor.authorSeferina, Mavroudi
dc.date.accessioned2015-08-04T07:02:18Z
dc.date.available2015-08-04T07:02:18Z
dc.date.issued2012
dc.identifier.isbn9783642304477
dc.identifier.issn03029743
dc.identifier.doi10.1007/978-3-642-30448-4_37
dc.identifier.urihttp://hdl.handle.net/10754/564489
dc.description.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.
dc.publisherSpringer Science + Business Media
dc.subjectevolutionary computation
dc.subjectgenetic algorithms
dc.subjectMachine Learning classification
dc.subjectmiRNA targets
dc.subjectmiRNAs
dc.subjectmultiobjective optimization
dc.subjectSupport Vector Machines
dc.titlePredicting human miRNA target genes using a novel evolutionary methodology
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalArtificial Intelligence: Theories and Applications
dc.conference.date28 May 2012 through 31 May 2012
dc.conference.name7th Hellenic Conference on Artificial Intelligence, SETN 2012
dc.conference.locationLamia
dc.contributor.institutionDepartment of Computer Engineering and Informatics, University of Patras, Greece
kaust.personKleftogiannis, Dimitrios A.


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