A zero altered Poisson random forest model for genomic-enabled prediction

dc.creatorMontesinos-Lopez, O.A.
dc.creatorMontesinos-López, A.
dc.creatorMosqueda-Gonzalez, B.A.
dc.creatorMontesinos-Lopez, J.C.
dc.creatorCrossa, J.
dc.creatorLozano-Ramirez, N.
dc.creatorSingh, P.K.
dc.creatorValladares-Anguiano, F.A.
dc.date2021-03-31T00:10:16Z
dc.date2021-03-31T00:10:16Z
dc.date2021
dc.date.accessioned2023-07-17T20:07:15Z
dc.date.available2023-07-17T20:07:15Z
dc.identifierhttps://hdl.handle.net/10883/21342
dc.identifier10.1093/g3journal/jkaa057
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7513123
dc.descriptionIn genomic selection choosing the statistical machine learning model is of paramount importance. In this paper, we present an application of a zero altered random forest model with two versions (ZAP_RF and ZAPC_RF) to deal with excess zeros in count response variables. The proposed model was compared with the conventional random forest (RF) model and with the conventional Generalized Poisson Ridge regression (GPR) using two real datasets, and we found that, in terms of prediction performance, the proposed zero inflated random forest model outperformed the conventional RF and GPR models.
dc.languageEnglish
dc.publisherGenetics Society of America
dc.relationhttp://hdl.handle.net/11529/10575
dc.relationhttp://hdl.handle.net/11529/10548438
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dc.rightsOpen Access
dc.source2
dc.source11
dc.source2160-1836
dc.sourceG3: Genes, Genomes, Genetics
dc.sourcejkaa057
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectGenomic Selection
dc.subjectCount Data
dc.subjectRandom Forest
dc.subjectZero Altered Poisson
dc.subjectGenomic Prediction
dc.subjectGenPred
dc.subjectShared Data Resource
dc.subjectMARKER-ASSISTED SELECTION
dc.subjectDATA
dc.subjectPLANT BREEDING
dc.subjectMODELS
dc.titleA zero altered Poisson random forest model for genomic-enabled prediction
dc.typeArticle
dc.typePublished Version
dc.coverageBethesda, MD (USA)


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