Cargando…
A zero altered Poisson random forest model for genomic-enabled prediction
In 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 compare...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8022945/ https://www.ncbi.nlm.nih.gov/pubmed/33693599 http://dx.doi.org/10.1093/g3journal/jkaa057 |
_version_ | 1783675032222826496 |
---|---|
author | Montesinos-López, Osval Antonio Montesinos-López, Abelardo Mosqueda-Gonzalez, Brandon A Montesinos-López, José Cricelio Crossa, José Ramirez, Nerida Lozano Singh, Pawan Valladares-Anguiano, Felícitas Alejandra |
author_facet | Montesinos-López, Osval Antonio Montesinos-López, Abelardo Mosqueda-Gonzalez, Brandon A Montesinos-López, José Cricelio Crossa, José Ramirez, Nerida Lozano Singh, Pawan Valladares-Anguiano, Felícitas Alejandra |
author_sort | Montesinos-López, Osval Antonio |
collection | PubMed |
description | In 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. |
format | Online Article Text |
id | pubmed-8022945 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-80229452021-04-09 A zero altered Poisson random forest model for genomic-enabled prediction Montesinos-López, Osval Antonio Montesinos-López, Abelardo Mosqueda-Gonzalez, Brandon A Montesinos-López, José Cricelio Crossa, José Ramirez, Nerida Lozano Singh, Pawan Valladares-Anguiano, Felícitas Alejandra G3 (Bethesda) Investigation In 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. Oxford University Press 2020-12-21 /pmc/articles/PMC8022945/ /pubmed/33693599 http://dx.doi.org/10.1093/g3journal/jkaa057 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of Genetics Society of America. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Investigation Montesinos-López, Osval Antonio Montesinos-López, Abelardo Mosqueda-Gonzalez, Brandon A Montesinos-López, José Cricelio Crossa, José Ramirez, Nerida Lozano Singh, Pawan Valladares-Anguiano, Felícitas Alejandra A zero altered Poisson random forest model for genomic-enabled prediction |
title | A zero altered Poisson random forest model for genomic-enabled prediction |
title_full | A zero altered Poisson random forest model for genomic-enabled prediction |
title_fullStr | A zero altered Poisson random forest model for genomic-enabled prediction |
title_full_unstemmed | A zero altered Poisson random forest model for genomic-enabled prediction |
title_short | A zero altered Poisson random forest model for genomic-enabled prediction |
title_sort | zero altered poisson random forest model for genomic-enabled prediction |
topic | Investigation |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8022945/ https://www.ncbi.nlm.nih.gov/pubmed/33693599 http://dx.doi.org/10.1093/g3journal/jkaa057 |
work_keys_str_mv | AT montesinoslopezosvalantonio azeroalteredpoissonrandomforestmodelforgenomicenabledprediction AT montesinoslopezabelardo azeroalteredpoissonrandomforestmodelforgenomicenabledprediction AT mosquedagonzalezbrandona azeroalteredpoissonrandomforestmodelforgenomicenabledprediction AT montesinoslopezjosecricelio azeroalteredpoissonrandomforestmodelforgenomicenabledprediction AT crossajose azeroalteredpoissonrandomforestmodelforgenomicenabledprediction AT ramirezneridalozano azeroalteredpoissonrandomforestmodelforgenomicenabledprediction AT singhpawan azeroalteredpoissonrandomforestmodelforgenomicenabledprediction AT valladaresanguianofelicitasalejandra azeroalteredpoissonrandomforestmodelforgenomicenabledprediction AT montesinoslopezosvalantonio zeroalteredpoissonrandomforestmodelforgenomicenabledprediction AT montesinoslopezabelardo zeroalteredpoissonrandomforestmodelforgenomicenabledprediction AT mosquedagonzalezbrandona zeroalteredpoissonrandomforestmodelforgenomicenabledprediction AT montesinoslopezjosecricelio zeroalteredpoissonrandomforestmodelforgenomicenabledprediction AT crossajose zeroalteredpoissonrandomforestmodelforgenomicenabledprediction AT ramirezneridalozano zeroalteredpoissonrandomforestmodelforgenomicenabledprediction AT singhpawan zeroalteredpoissonrandomforestmodelforgenomicenabledprediction AT valladaresanguianofelicitasalejandra zeroalteredpoissonrandomforestmodelforgenomicenabledprediction |