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dc.contributor.authorKaraboga, D.
dc.contributor.authorAslan, S.
dc.date.accessioned2020-06-21T13:39:49Z
dc.date.available2020-06-21T13:39:49Z
dc.date.issued2016
dc.identifier.issn1676-5680
dc.identifier.urihttps://doi.org/10.4238/gmr.15028645
dc.identifier.urihttps://hdl.handle.net/20.500.12712/13697
dc.descriptionKaraboga, Dervis/0000-0003-1439-6969en_US
dc.descriptionWOS: 000384881000145en_US
dc.descriptionPubMed: 27173272en_US
dc.description.abstractThe great majority of biological sequences share significant similarity with other sequences as a result of evolutionary processes, and identifying these sequence similarities is one of the most challenging problems in bioinformatics. In this paper, we present a discrete artificial bee colony (ABC) algorithm, which is inspired by the intelligent foraging behavior of real honey bees, for the detection of highly conserved residue patterns or motifs within sequences. Experimental studies on three different data sets showed that the proposed discrete model, by adhering to the fundamental scheme of the ABC algorithm, produced competitive or better results than other metaheuristic motif discovery techniques.en_US
dc.language.isoengen_US
dc.publisherFunpec-Editoraen_US
dc.relation.isversionof10.4238/gmr.15028645en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBioinformaticsen_US
dc.subjectMotif discoveryen_US
dc.subjectBinding sitesen_US
dc.subjectArtificial bee colonyen_US
dc.subjectDiscrete optimizationen_US
dc.titleA discrete artificial bee colony algorithm for detecting transcription factor binding sites in DNA sequencesen_US
dc.typearticleen_US
dc.contributor.departmentOMÜen_US
dc.identifier.volume15en_US
dc.identifier.issue2en_US
dc.relation.journalGenetics and Molecular Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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