| |
|
|
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Café. |
|
Data corrente: |
15/10/2020 |
|
Data da última atualização: |
15/10/2020 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Autoria: |
SOUSA, I. C. de; NASCIMENTO, M.; SILVA, G. N.; NASCIMENTO, A. C. C.; CRUZ, C. D.; SILVA, F. F. e; ALMEIDA, D. P. de; PESTANA, K. N.; AZEVEDO, C. F.; ZAMBOLIM, L.; CAIXETA, E. T. |
|
Afiliação: |
Ithalo Coelho de Sousa, Universidade Federal de Viçosa; Moysés Nascimento, Universidade Federal de Viçosa; Gabi Nunes Silva, Universidade Federal de Rondônia; Ana Carolina Campana Nascimento, Universidade Federal de Viçosa; Cosme Damião Cruz, Universidade Federal de Viçosa; Fabyano Fonseca e Silva, Universidade Federal de Viçosa; Dênia Pires de Almeida, Universidade Federal de Viçosa; Kátia Nogueira Pestana, Embrapa Mandioca e Fruticultura; Camila Ferreira Azevedo, Universidade Federal de Viçosa; Laércio Zambolim, Universidade Federal de Viçosa; EVELINE TEIXEIRA CAIXETA MOURA, CNPCa. |
|
Título: |
Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms. |
|
Ano de publicação: |
2021 |
|
Fonte/Imprenta: |
Scientia Agricola, v. 78, n. 4, e20200021, 2021. |
|
DOI: |
http://dx.doi.org/10.1590/1678-992X-2020-0021 |
|
Idioma: |
Inglês |
|
Conteúdo: |
Genomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature. MenosGenomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs)... Mostrar Tudo |
|
Palavras-Chave: |
Statistical learning. |
|
Thesagro: |
Hemileia Vastatrix. |
|
Thesaurus Nal: |
Artificial intelligence; Plant breeding. |
|
Categoria do assunto: |
-- |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/216675/1/Sousa-et-al-2020.pdf
|
|
Marc: |
LEADER 02472naa a2200301 a 4500
001 2125524
005 2020-10-15
008 2021 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.1590/1678-992X-2020-0021$2DOI
100 1 $aSOUSA, I. C. de
245 $aGenomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms.$h[electronic resource]
260 $c2021
520 $aGenomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature.
650 $aArtificial intelligence
650 $aPlant breeding
650 $aHemileia Vastatrix
653 $aStatistical learning
700 1 $aNASCIMENTO, M.
700 1 $aSILVA, G. N.
700 1 $aNASCIMENTO, A. C. C.
700 1 $aCRUZ, C. D.
700 1 $aSILVA, F. F. e
700 1 $aALMEIDA, D. P. de
700 1 $aPESTANA, K. N.
700 1 $aAZEVEDO, C. F.
700 1 $aZAMBOLIM, L.
700 1 $aCAIXETA, E. T.
773 $tScientia Agricola$gv. 78, n. 4, e20200021, 2021.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Café (CNPCa) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Amazônia Ocidental. Para informações adicionais entre em contato com cpaa.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Amazônia Ocidental. |
|
Data corrente: |
14/02/2012 |
|
Data da última atualização: |
15/04/2014 |
|
Tipo da produção científica: |
Artigo em Anais de Congresso |
|
Autoria: |
CHAVES, F. C. M.; BATISTA, A. C.; HIDALGO, A. de F.; MING, L. C.; BIZZO, H. R.; SOUZA, A. M. de; PINTO, M. A. S. |
|
Afiliação: |
FRANCISCO CELIO MAIA CHAVES, CPAA; A. C. Batista, UFAM; A. de F. Hidalgo, UFAM; Lin Chau Ming, UNESP; HUMBERTO RIBEIRO BIZZO, CTAA; ANDRESSA MOREIRA DE SOUZA, CTAA; MARCO ANTONIO SILVA PINTO, CTAA. |
|
Título: |
Biomass production and chemical composition of essential oil of Piper callosum as affected by spacing in Manaus, Amazonas State, Brazil. |
|
Ano de publicação: |
2011 |
|
Fonte/Imprenta: |
Acta Horticulturae, v. 925, p. 233-236, 2011. Edition of Proceedings of the XXVIII International Horticultural Congress on Science and Horticulture for People, Lisbon, Aug. 2010. |
|
Idioma: |
Inglês |
|
Conteúdo: |
The family Piperaceae contains nearly 2000 species, most of them distributed in two genera, Piper and Peperomia. In Brazil circa 170 Piper species are found, mainly in tropical areas Their use ranges from flavoring and culinary to raw material for pharmaceutical and cosmetic industry. One of these species, Piper callosum, is used in folk medicine in the Amazon area. The objective of this study was to evaluate the production of biomass (aerial parts) as well as yield and composition of the essential oil from the leaves, according to different spacing between cultivated plants at Embrapa Western Amazon, in Manaus, State of Amazonas, Brazil. |
|
Palavras-Chave: |
Amazonas; Piper callosum; Plantas medicinais. |
|
Thesagro: |
Biomassa; Óleo Essencial; Piperaceae; Planta Medicinal. |
|
Thesaurus NAL: |
safrole. |
|
Categoria do assunto: |
-- |
|
Marc: |
LEADER 01590nam a2200277 a 4500
001 1915392
005 2014-04-15
008 2011 bl uuuu u00u1 u #d
100 1 $aCHAVES, F. C. M.
245 $aBiomass production and chemical composition of essential oil of Piper callosum as affected by spacing in Manaus, Amazonas State, Brazil.
260 $aActa Horticulturae, v. 925, p. 233-236, 2011. Edition of Proceedings of the XXVIII International Horticultural Congress on Science and Horticulture for People, Lisbon, Aug. 2010.$c2010
520 $aThe family Piperaceae contains nearly 2000 species, most of them distributed in two genera, Piper and Peperomia. In Brazil circa 170 Piper species are found, mainly in tropical areas Their use ranges from flavoring and culinary to raw material for pharmaceutical and cosmetic industry. One of these species, Piper callosum, is used in folk medicine in the Amazon area. The objective of this study was to evaluate the production of biomass (aerial parts) as well as yield and composition of the essential oil from the leaves, according to different spacing between cultivated plants at Embrapa Western Amazon, in Manaus, State of Amazonas, Brazil.
650 $asafrole
650 $aBiomassa
650 $aÓleo Essencial
650 $aPiperaceae
650 $aPlanta Medicinal
653 $aAmazonas
653 $aPiper callosum
653 $aPlantas medicinais
700 1 $aBATISTA, A. C.
700 1 $aHIDALGO, A. de F.
700 1 $aMING, L. C.
700 1 $aBIZZO, H. R.
700 1 $aSOUZA, A. M. de
700 1 $aPINTO, M. A. S.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Amazônia Ocidental (CPAA) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
