Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Brandon Alejandro Mosqueda González"'
Autor:
Osval A. Montesinos-López, Sofia Ramos-Pulido, Carlos Moisés Hernández-Suárez, Brandon Alejandro Mosqueda González, Felícitas Alejandra Valladares-Anguiano, Paolo Vitale, Abelardo Montesinos-López, José Crossa
Publikováno v:
Frontiers in Plant Science, Vol 14 (2023)
IntroductionGenomic selection (GS) has gained global importance due to its potential to accelerate genetic progress and improve the efficiency of breeding programs.Objectives of the researchIn this research we proposed a method to improve the predict
Externí odkaz:
https://doaj.org/article/73541120d2894d3b9cbd8a631542b77a
Autor:
Osval A. Montesinos López, Brandon Alejandro Mosqueda González, Abelardo Montesinos López, José Crossa
Publikováno v:
Genes, Vol 14, Iss 5, p 1003 (2023)
Genomic selection (GS) is revolutionizing plant breeding. However, because it is a predictive methodology, a basic understanding of statistical machine-learning methods is necessary for its successful implementation. This methodology uses a reference
Externí odkaz:
https://doaj.org/article/47476b1b221f4828a03639289ca05086
Autor:
Osval Antonio Montesinos López, Brandon Alejandro Mosqueda González, Abel Palafox González, Abelardo Montesinos López, José Crossa
Publikováno v:
Frontiers in Genetics, Vol 13 (2022)
The adoption of machine learning frameworks in areas beyond computer science have been facilitated by the development of user-friendly software tools that do not require an advanced understanding of computer programming. In this paper, we present a n
Externí odkaz:
https://doaj.org/article/2a09f85047494f8792fe24e59eb02321
Autor:
Osval A. Montesinos-López, Leonardo Crespo-Herrera, Carolina Saint Pierre, Bernabe Cano-Paez, Gloria Isabel Huerta-Prado, Brandon Alejandro Mosqueda-González, Sofia Ramos-Pulido, Guillermo Gerard, Khalid Alnowibet, Roberto Fritsche-Neto, Abelardo Montesinos-López, José Crossa
Publikováno v:
Frontiers in Plant Science, Vol 15 (2024)
IntroductionBecause Genomic selection (GS) is a predictive methodology, it needs to guarantee high-prediction accuracies for practical implementations. However, since many factors affect the prediction performance of this methodology, its practical i
Externí odkaz:
https://doaj.org/article/bc352c418ea44b3aa88d1cf294b125fa