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dc.contributor.authorVeiner, Marcell
dc.contributor.authorMorimoto Borges, Juliano
dc.contributor.authorLeadbeater, Ellouise
dc.contributor.authorManfredini, Fabio
dc.date.accessioned2022-07-04T15:36:01Z
dc.date.available2022-07-04T15:36:01Z
dc.date.issued2022-08-01
dc.identifier.citationVeiner , M , Morimoto Borges , J , Leadbeater , E & Manfredini , F 2022 , ' Machine Learning models identify gene predictors of waggle dance behaviour in honeybees ' , Molecular Ecology Resources , vol. 22 , no. 6 , 14 , pp. 2248-2261 . https://doi.org/10.1111/1755-0998.13611en
dc.identifier.issn1755-098X
dc.identifier.otherPURE: 214914822
dc.identifier.otherPURE UUID: 397ce1ff-c7bc-41ab-9246-f7fe85c0d9d2
dc.identifier.otherPubMed: 35334147
dc.identifier.otherScopus: 85128281497
dc.identifier.urihttps://hdl.handle.net/2164/18780
dc.descriptionWe thank Dr Georgios Leontidis (The School of Natural and Computing Science, University of Aberdeen) for his valuable support during the selection and implementation of the ML models, and the two anonymous reviewers for providing useful feedback that helped improve the clarity and soundness of the manuscript. We are also grateful to NERC (Natural Environment Research Council) for funding this project and supporting MV’s salary over 10 weeks through their Research Experience Placement programme (DTG reference: NE/S007377/1). The honeybee work that was performed to obtain the sequencing data used in this study was funded by the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant no. 638873 to EL). This funding also supported FM during the execution of the field and molecular work.en
dc.language.isoeng
dc.relation.ispartofMolecular Ecology Resourcesen
dc.rightsThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.https://creativecommons.org/licenses/by/4.0/en
dc.subjectbioinfomaticsen
dc.subjectfeature selectionen
dc.subjectgenomicsen
dc.subjectgene structure and functionen
dc.subjectinsectsen
dc.subjectsocial evolutionen
dc.subjectQH301 Biologyen
dc.subjectNatural Environment Research Council (NERC)en
dc.subjectNE/S007377/1en
dc.subjectEuropean Commissionen
dc.subject638873en
dc.subjectSupplementary Dataen
dc.subjectSupplementary Informationen
dc.subject.lccQH301en
dc.titleMachine Learning models identify gene predictors of waggle dance behaviour in honeybeesen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Biological Sciencesen
dc.description.statusPeer revieweden
dc.description.versionPublisher PDFen
dc.identifier.doihttps://doi.org/10.1111/1755-0998.13611


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