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-  2016 

“Noisy beets”: impact of phenotyping errors on genomic predictions for binary traits in Beta vulgaris

DOI: 10.1186/s13007-016-0136-4

Keywords: Noisy data, Classification, K-nearest neighbours (KNN), Random forest (RF), Support vector machines (SVM), Ridge logistic regression, Sugar beet, Binomial phenotype, Robustness to errors, Genomic predictions

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Abstract:

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

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