%0 Journal Article
%T ˇ°Noisy beetsˇ±: impact of phenotyping errors on genomic predictions for binary traits in Beta vulgaris
%A Chiara Broccanello
%A Filippo Biscarini
%A Nelson Nazzicari
%A Piergiorgio Stevanato
%A Simone Marini
%J Archive of "Plant Methods".
%D 2016
%R 10.1186/s13007-016-0136-4
%X Noise (errors) in scientific data is endemic and may have a detrimental effect on statistical analyses and experimental results. The effects of noisy data have been assessed in genome-wide association studies for case-control experiments in human medicine. Little is known, however, on the impact of noisy data on genomic predictions, a widely used statistical application in plant and animal breeding
%K Noisy data
%K Classification
%K K-nearest neighbours (KNN)
%K Random forest (RF)
%K Support vector machines (SVM)
%K Ridge logistic regression
%K Sugar beet
%K Binomial phenotype
%K Robustness to errors
%K Genomic predictions
%U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4949885/