Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1180566
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dc.contributor.authorSENA, T. M.
dc.contributor.authorBRAGA, L. G.
dc.contributor.authorCAETANO, S. L.
dc.contributor.authorRAMOS, S. B.
dc.contributor.authorSILVA, M. V. G. B.
dc.contributor.authorMUARI, D. P.
dc.date.accessioned2025-10-23T19:49:09Z-
dc.date.available2025-10-23T19:49:09Z-
dc.date.created2025-10-23
dc.date.issued2025
dc.identifier.citationIn: SIMPÓSIO BRASILEIRO DE MELHORAMENTO ANIMAL, 16., 2025, Piracicaba. Anais [...]. Juiz de Fora: Sociedade Brasileira de Melhoramento Animal, 2025.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1180566-
dc.descriptionA supervised machine learning regression analysis was conducted using the Random Forest (RF) algorithm to classify SNPs according to their predictive importance. The response variable was 305-day milk yield (MY305) in Gir cattle, and the predictors included SNPs with or without fixed effects, depending on the tested scenarios. The analyses were performed using the “ranger” function implemented in R. I
dc.language.isoeng
dc.rightsopenAccess
dc.subjectModelagem
dc.titleWhat you include matters: modeling choices influence genomic prediction and QTL detection.
dc.typeResumo em anais e proceedings
dc.subject.thesagroGenética
dc.subject.thesagroBovino
dc.subject.thesagroProdução Leiteira
dc.subject.thesagroGado Leiteiro
dc.subject.thesagroGado Gir
dc.description.notesTema: Integrando campo e tecnologia.
dc.format.extent2p. 278.
riaa.ainfo.id1180566
riaa.ainfo.lastupdate2025-10-23
dc.contributor.institutionTHOMAZ MARQUES SENA, UNIVERSIDADE ESTADUAL PAULISTA; LARISSA GRACIANO BRAGA; SABRINA LUIZA CAETANO; SALVADOR BOCCALETTI RAMOS; MARCOS VINICIUS GUALBERTO BARBOSA DA SILVA, CNPGL; DANÍSIO PRADO MUARI.
Appears in Collections:Resumo em anais de congresso (CNPGL)

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