Now showing items 1-9 of 9
A genomic bayesian multi-trait and multi-environment model
(Genetics Society of America, 2016)
When information on multiple genotypes evaluated in multiple environments is recorded, a multi-environment single trait model for assessing genotype · environment interaction (G · E) is usually employed. Comprehensive ...
Sample size under inverse negative binomial group testing for accuracy in parameter estimation
(Public Library of Science, 2012)
Background. The group testing method has been proposed for the detection and estimation of genetically modified plants (adventitious presence of unwanted transgenic plants, AP). For binary response variables (presence or ...
Prediction of multiple-trait and multiple-environment genomic data using recommender systems
(Genetics Society of America, 2018)
In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. ...
A benchmarking between deep learning, support vector machine and bayesian threshold best linear unbiased prediction for predicting ordinal traits in plant breeding
(Genetics Society of America, 2019)
Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical models for ordinal phenotypes to improve the accuracy of the selection of candidate genotypes. For this reason, in this ...
Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data
(BioMed Central, 2017)
Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, ...
A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction
(Genetics Society of America, 2017)
When a plant scientist wishes to make genomic-enabled predictions of multiple traits measured in multiple individuals in multiple environments, the most common strategy for performing the analysis is to use a single trait ...
Bayesian functional regression as an alternative statistical analysis of high‑throughput phenotyping data of modern agriculture
(BioMed Central, 2018)
Background: Modern agriculture uses hyperspectral cameras with hundreds of reflectance data at discrete narrow bands measured in several environments. Recently, Montesinos-López et al. (Plant Methods 13(4):1–23, 2017a. ...
Correction to: bayesian functional regression as an alternative statistical analysis of high-throughput phenotyping data of modern agriculture
(BioMed Central, 2018)
Unfortunately, in the original version  of this article, a funder note was missed out in the acknowledgement. Te corrected acknowledgement is given below: Acknowledgements Te authors thank all the feld and lab assistants ...
Genomic prediction of genotype x environment interaction kernel regression models
(Crop Science Society of America, 2016)
In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel. In this ...