Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/56324
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dc.contributor.authorFerdosi, M Hen
dc.contributor.authorMasoodi, Sen
dc.contributor.authorKhansefid, Men
local.source.editorEditor(s): Veerkamp, R F and de Haas, Yen
dc.date.accessioned2023-10-10T02:57:15Z-
dc.date.available2023-10-10T02:57:15Z-
dc.date.issued2022-
dc.identifier.citationProceedings of the 12th World Congress on Genetics Applied to Livestock Production, v.12, p. 1450-1453en
dc.identifier.isbn978-90-8686-940-4en
dc.identifier.urihttps://hdl.handle.net/1959.11/56324-
dc.description.abstract<p>In this paper, we introduced two novel algorithms to identify duplicated genotypes. The runtime of these algorithms was compared with the widely adopted Exhaustive Search algorithm using simulated data. We found that both new algorithms could significantly reduce the execution time. Further, the optimised Matrix Algebra Approach algorithm was faster than the Dis-Similarity lookup table and could improve the performance nearly 34 times compared to Exhaustive Search</p>en
dc.languageenen
dc.publisherWageningen Academic Publishersen
dc.relation.ispartofProceedings of the 12th World Congress on Genetics Applied to Livestock Productionen
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleEfficient algorithms to identify duplicated genotypes in large datasetsen
dc.typeConference Publicationen
dc.relation.conference12th World Congress on Genetics Applied to Livestock Productionen
dc.identifier.doi10.3920/978-90-8686-940-4_346en
dcterms.accessRightsUNE Greenen
local.contributor.firstnameM Hen
local.contributor.firstnameSen
local.contributor.firstnameMen
local.profile.schoolAnimal Genetics and Breeding Uniten
local.profile.emailmferdos3@une.edu.auen
local.output.categoryE1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.date.conference3 – 8 July, 2022en
local.conference.placeRotterdam, The Netherlandsen
local.publisher.placeWageningen, The Netherlandsen
local.identifier.runningnumber346en
local.format.startpage1450en
local.format.endpage1453en
local.peerreviewedYesen
local.identifier.volume12en
local.access.fulltextYesen
local.contributor.lastnameFerdosien
local.contributor.lastnameMasoodien
local.contributor.lastnameKhansefiden
dc.identifier.staffune-id:mferdos3en
local.profile.orcid0000-0001-5385-4913en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/56324en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleEfficient algorithms to identify duplicated genotypes in large datasetsen
local.relation.fundingsourcenoteThis study was supported by Meat and Livestock Australia project L.GEN.1704.en
local.output.categorydescriptionE1 Refereed Scholarly Conference Publicationen
local.relation.urlhttps://www.wageningenacademic.com/doi/10.3920/978-90-8686-940-4_346en
local.conference.details12th World Congress on Genetics Applied to Livestock Production, Rotterdam, The Netherlands, 3 – 8 July, 2022en
local.search.authorFerdosi, M Hen
local.search.authorMasoodi, Sen
local.search.authorKhansefid, Men
local.open.fileurlhttps://rune.une.edu.au/web/retrieve/aabb9446-e9b7-480b-b98e-22a2cc4a10a8en
local.uneassociationYesen
dc.date.presented2022-07-05-
local.atsiresearchNoen
local.conference.venueDe Doelen International Conference Center Rotterdam Schouwburgplein 50 Rotterdam, The Netherlandsen
local.sensitive.culturalNoen
local.year.published2022en
local.year.presented2022en
local.fileurl.openhttps://rune.une.edu.au/web/retrieve/aabb9446-e9b7-480b-b98e-22a2cc4a10a8en
local.fileurl.openpublishedhttps://rune.une.edu.au/web/retrieve/aabb9446-e9b7-480b-b98e-22a2cc4a10a8en
local.subject.for2020300305 Animal reproduction and breedingen
local.subject.for2020310506 Gene mappingen
local.subject.for2020310509 Genomicsen
local.subject.seo2020280101 Expanding knowledge in the agricultural, food and veterinary sciencesen
local.date.start2022-07-03-
local.date.end2022-07-08-
local.profile.affiliationtypeUNE Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
Appears in Collections:Animal Genetics and Breeding Unit (AGBU)
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