Genetic prediction in bovine meat production : Is worth integrating bayesian and machine learning approaches? A comprenhensive analysis
Resumen:
Genomic prediction is a still growing field, as good predictions can have important economic impact in both, agronomics and health. In this article, we make a brief review and a comprehensive analysis of classical predictors used in the area. We propose a strategy to choose and ensemble of methods and to combine their results, to take advantage of the complementarity that some predictors have.
| 2015 | |
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Parametric Non parametric Genomic Selection Prediction Fusion Procesamiento de Señales |
|
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/42653 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522940278800384 |
|---|---|
| author | Fariello, Maria Ines |
| author2 | Armstrong, Eileen Fernández, Alicia |
| author2_role | author author |
| author_facet | Fariello, Maria Ines Armstrong, Eileen Fernández, Alicia |
| author_role | author |
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| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Fariello, Maria Ines Armstrong, Eileen Fernández, Alicia |
| dc.date.accessioned.none.fl_str_mv | 2024-02-26T19:52:28Z |
| dc.date.available.none.fl_str_mv | 2024-02-26T19:52:28Z |
| dc.date.issued.es.fl_str_mv | 2015 |
| dc.date.submitted.es.fl_str_mv | 20240223 |
| dc.description.abstract.none.fl_txt_mv | Genomic prediction is a still growing field, as good predictions can have important economic impact in both, agronomics and health. In this article, we make a brief review and a comprehensive analysis of classical predictors used in the area. We propose a strategy to choose and ensemble of methods and to combine their results, to take advantage of the complementarity that some predictors have. |
| dc.identifier.citation.es.fl_str_mv | Fariello, M.I., Amstrong, E., Fernandez, A. "Genetic prediction in bovine meat production: is worth integrating bayesian and machine learning approaches? A comprenhensive analysis" Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Science, vol 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_2 |
| dc.identifier.doi.es.fl_str_mv | DOI: 10.1007/978-3-319-25751-8 2 |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/42653 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | Springer International Publishing |
| dc.relation.ispartof.es.fl_str_mv | 20th Iberoamerican Congress, CIARP 2015, Montevideo, Uruguay, 9-12 nov, 2015 |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.es.fl_str_mv | Parametric Non parametric Genomic Selection Prediction Fusion |
| dc.subject.other.es.fl_str_mv | Procesamiento de Señales |
| dc.title.none.fl_str_mv | Genetic prediction in bovine meat production : Is worth integrating bayesian and machine learning approaches? A comprenhensive analysis |
| dc.type.es.fl_str_mv | Ponencia |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/conferenceObject |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | Genomic prediction is a still growing field, as good predictions can have important economic impact in both, agronomics and health. In this article, we make a brief review and a comprehensive analysis of classical predictors used in the area. We propose a strategy to choose and ensemble of methods and to combine their results, to take advantage of the complementarity that some predictors have. |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | COLIBRI_5abf8f0b0dd44cc0a80a2af472c670ca |
| identifier_str_mv | Fariello, M.I., Amstrong, E., Fernandez, A. "Genetic prediction in bovine meat production: is worth integrating bayesian and machine learning approaches? A comprenhensive analysis" Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Science, vol 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_2 DOI: 10.1007/978-3-319-25751-8 2 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/42653 |
| publishDate | 2015 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | 2024-02-26T19:52:28Z2024-02-26T19:52:28Z201520240223Fariello, M.I., Amstrong, E., Fernandez, A. "Genetic prediction in bovine meat production: is worth integrating bayesian and machine learning approaches? A comprenhensive analysis" Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Science, vol 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_2https://hdl.handle.net/20.500.12008/42653DOI: 10.1007/978-3-319-25751-8 2Genomic prediction is a still growing field, as good predictions can have important economic impact in both, agronomics and health. In this article, we make a brief review and a comprehensive analysis of classical predictors used in the area. We propose a strategy to choose and ensemble of methods and to combine their results, to take advantage of the complementarity that some predictors have.Made available in DSpace on 2024-02-26T19:52:28Z (GMT). No. of bitstreams: 5 FAF15.pdf: 232524 bytes, checksum: 060b12411eb2d3306f91e50518a46dcc (MD5) license_text: 21936 bytes, checksum: 9833653f73f7853880c94a6fead477b1 (MD5) license_url: 49 bytes, checksum: 4afdbb8c545fd630ea7db775da747b2f (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) license.txt: 4244 bytes, checksum: 528b6a3c8c7d0c6e28129d576e989607 (MD5) Previous issue date: 2015enengSpringer International Publishing20th Iberoamerican Congress, CIARP 2015, Montevideo, Uruguay, 9-12 nov, 2015Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad De La República. (Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)ParametricNon parametricGenomicSelectionPredictionFusionProcesamiento de SeñalesGenetic prediction in bovine meat production : Is worth integrating bayesian and machine learning approaches? 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- Universidad de la Repúblicafalse |
| spellingShingle | Genetic prediction in bovine meat production : Is worth integrating bayesian and machine learning approaches? A comprenhensive analysis Fariello, Maria Ines Parametric Non parametric Genomic Selection Prediction Fusion Procesamiento de Señales |
| status_str | publishedVersion |
| title | Genetic prediction in bovine meat production : Is worth integrating bayesian and machine learning approaches? A comprenhensive analysis |
| title_full | Genetic prediction in bovine meat production : Is worth integrating bayesian and machine learning approaches? A comprenhensive analysis |
| title_fullStr | Genetic prediction in bovine meat production : Is worth integrating bayesian and machine learning approaches? A comprenhensive analysis |
| title_full_unstemmed | Genetic prediction in bovine meat production : Is worth integrating bayesian and machine learning approaches? A comprenhensive analysis |
| title_short | Genetic prediction in bovine meat production : Is worth integrating bayesian and machine learning approaches? A comprenhensive analysis |
| title_sort | Genetic prediction in bovine meat production : Is worth integrating bayesian and machine learning approaches? A comprenhensive analysis |
| topic | Parametric Non parametric Genomic Selection Prediction Fusion Procesamiento de Señales |
| url | https://hdl.handle.net/20.500.12008/42653 |
