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Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging
(2021-07-01)
The use of DNA methylation signatures to predict chronological age and aging rate is of interest in many fields, including disease prevention and treatment, forensics, and anti-aging medicine. Although a large number of ...
Genomic prediction in CIMMYT maize and wheat breeding programs
(Springer Naturehttp://www.nature.com/hdy/journal/vaop/ncurrent/abs/hdy201316a.html, 2014)
Estimación y predicción del modelo ARCH para la volatilidad a través de embebimientos de distribuciones de probabilidad en espacios de Hilbert con kernel reproductivo
(Universidad Tecnológica de PereiraFacultad de Ciencias BásicasPereiraMaestría en Matemática, 2023)
Métricas entre procesos aleatorios usando el método de embebimiento de distribuciones de probabilidad en un espacio de Hilbert con kernel reproductivo
(Universidad Tecnológica de PereiraPereira, 2023)
A comparison of statistical methods for genomic selection in a mice population
(Biomed Central Ltd., 2012-11-08)
Background: The availability of high-density panels of SNP markers has opened new perspectives for marker-assisted selection strategies, such that genotypes for these markers are used to predict the genetic merit of selection ...
A comparison of statistical methods for genomic selection in a mice population
(Biomed Central Ltd., 2012-11-08)
Background: The availability of high-density panels of SNP markers has opened new perspectives for marker-assisted selection strategies, such that genotypes for these markers are used to predict the genetic merit of selection ...
A comparison of statistical methods for genomic selection in a mice population
(Biomed Central Ltd., 2014)
Genomic prediction of genotype x environment interaction kernel regression models
(Crop Science Society of America, 2019)
Relevant multichannel time series representation based on functional measures in RKHS
(Manizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - AutomáticaDepartamento de Ingeniería Eléctrica y ElectrónicaUniversidad Nacional de Colombia - Sede Manizales, 2020)
Kernels methods provide a powerful and unifying framework to solve nonlinear problems while retaining in many cases, the simplicity of linear solutions. However, in machine learning and kernels methods, data is assumed to ...
Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat
(Genetics Society of Americahttp://www.g3journal.org/content/2/12/1595.full, 2013)