Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Isabela de Castro Sant'Anna"'
Autor:
Gabriela França Oliveira, Ana Carolina Campana Nascimento, Camila Ferreira Azevedo, Maurício de Oliveira Celeri, Laís Mayara Azevedo Barroso, Isabela de Castro Sant’Anna, José Marcelo Soriano Viana, Marcos Deon Vilela de Resende, Moysés Nascimento
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-10 (2023)
Abstract The aim of this study was to evaluate the performance of Quantile Regression (QR) in Genome-Wide Association Studies (GWAS) regarding the ability to detect QTLs (Quantitative Trait Locus) associated with phenotypic traits of interest, consid
Externí odkaz:
https://doaj.org/article/c8f503afa7e047199e44ff82de5f7c2c
Autor:
Vinícius Quintão Carneiro, Jussara Mencalha, Isabela de Castro Sant’anna, Gabi Nunes Silva, Júlio Augusto de Castro Miguel, Pedro Crescêncio Souza Carneiro, Moysés Nascimento, Cosme Damião Cruz
Publikováno v:
Acta Scientiarum: Agronomy, Vol 45, Iss 1 (2023)
The genotype by environment interaction is the main factor that influences the response of evaluated genotypes in trials of value for cultivation and use. Adaptability and stability analyses are fundamental to understanding the performance of genotyp
Externí odkaz:
https://doaj.org/article/53a88bff7ce4404495cf6ce4b85c14f5
Autor:
Cristiane Botelho Valadares, Moysés Nascimento, Maurício de Oliveira Celeri, Ana Carolina Campana Nascimento, Laís Mayara Azevedo Barroso, Isabela de Castro Sant’Anna, Camila Ferreira Azevedo
Publikováno v:
Ciência Rural, Vol 53, Iss 10 (2023)
ABSTRACT: Quantile Random Forest (QRF) is a non-parametric methodology that combines the advantages of Random Forest (RF) and Quantile Regression (QR). Specifically, this approach can explore non-linear functions, determining the probability distribu
Externí odkaz:
https://doaj.org/article/38f408c6b9a0423b94ae962a949cead4
Autor:
Isabela de Castro Sant’Anna, Ligia Regina Lima Gouvêa, Maria Alice Martins, Erivaldo José Scaloppi Junior, Rogério Soares de Freitas, Paulo de Souza Gonçalves
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
Abstract The objective of this study was to evaluate the genetic variability of natural rubber latex traits among 44 elite genotypes of the rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Müell. Arg.]. Multivariate analysis and machine lea
Externí odkaz:
https://doaj.org/article/35af4e11c9404c0ebe15260c6a810bef
Autor:
Ithalo Coelho de Sousa, Moysés Nascimento, Isabela de Castro Sant’anna, Eveline Teixeira Caixeta, Camila Ferreira Azevedo, Cosme Damião Cruz, Felipe Lopes da Silva, Emilly Ruas Alkimim, Ana Carolina Campana Nascimento, Nick Vergara Lopes Serão
Publikováno v:
PLoS ONE, Vol 17, Iss 1 (2022)
Many methodologies are used to predict the genetic merit in animals and plants, but some of them require priori assumptions that may increase the complexity of the model. Artificial neural network (ANN) has advantage to not require priori assumptions
Externí odkaz:
https://doaj.org/article/97436dfdcf284d7b95029c3fa65db544
Autor:
Antônio Carlos da Silva Júnior, Isabela de Castro Sant’Anna, Michele Jorge Silva Siqueira, Cosme Damião Cruz, Camila Ferreira Azevedo, Moyses Nascimento, Plínio César Soares
Publikováno v:
PLoS ONE, Vol 17, Iss 5 (2022)
The biggest challenge for the reproduction of flood-irrigated rice is to identify superior genotypes that present development of high-yielding varieties with specific grain qualities, resistance to abiotic and biotic stresses in addition to superior
Externí odkaz:
https://doaj.org/article/0e480c5cac7a4de3bbd0c2bd2d3ccdef
Autor:
Gabriela França Oliveira, Ana Carolina Campana Nascimento, Moysés Nascimento, Isabela de Castro Sant'Anna, Juan Vicente Romero, Camila Ferreira Azevedo, Leonardo Lopes Bhering, Eveline Teixeira Caixeta Moura
Publikováno v:
PLoS ONE, Vol 16, Iss 1, p e0243666 (2021)
This study assessed the efficiency of Genomic selection (GS) or genome-wide selection (GWS), based on Regularized Quantile Regression (RQR), in the selection of genotypes to breed autogamous plant populations with oligogenic traits. To this end, simu
Externí odkaz:
https://doaj.org/article/f881be0e5fc94f2b977068b61417f40b
Autor:
Antônio Carlos da Silva Júnior, Michele Jorge da Silva, Cosme Damião Cruz, Isabela de Castro Sant'Anna, Gabi Nunes Silva, Moysés Nascimento, Camila Ferreira Azevedo
Publikováno v:
PLoS ONE, Vol 16, Iss 11, p e0257213 (2021)
The present study evaluated the importance of auxiliary traits of a principal trait based on phenotypic information and previously known genetic structure using computational intelligence and machine learning to develop predictive tools for plant bre
Externí odkaz:
https://doaj.org/article/a37b62ff0ad94b229f02e30e7fbcd323
Publikováno v:
Acta Scientiarum: Agronomy, Vol 43, Pp e46307-e46307 (2020)
This paper aimed to evaluate the effectiveness of subset selection of markers for genome-enabled prediction of genetic values using radial basis function neural networks (RBFNN). To this end, an F1 population derived from the hybridization of diverge
Externí odkaz:
https://doaj.org/article/89d7549153ad453faa03d983f46951ca
Autor:
Gabi Nunes Silva, Antônio Carlos da Silva Júnior, Isabela de Castro Sant’Anna, Cosme Damião Cruz, Moysés Nascimento, Plínio César Soares
Publikováno v:
Pesquisa Agropecuária Brasileira, Vol 55 (2020)
Abstract: The objective of this work was to evaluate the similarity network graphic methodology for the classification of flood-irrigated rice (Orzya sativa) genotypes regarding their adaptability and stability. Two statistical measures were used to
Externí odkaz:
https://doaj.org/article/25c500618f4145d78570d480108eef4a