The importance of disease incidence rate on performance of GBLUP, threshold BayesA and machine learning methods in original and imputed data set

The importance of disease incidence rate on performance of GBLUP, threshold BayesA and machine learning methods in original and imputed data set

Aim of study: To predict genomic accuracy of binary traits considering different rates of disease incidence. Area of study: Simulation Material and methods: Two machine learning algorithms including Boosting and Random Forest (RF) as well as threshold BayesA (TBA) and genomic BLUP (GBLUP) were emplo...

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Bibliographic Details
Main Authors: Yousef Naderi, Saadat Sadeghi
Format: Article
Language:English
Published: Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria 2020-12-01
Series:Spanish Journal of Agricultural Research
Subjects:
accuracy
boosting
disease susceptibility
imputation
random forest
Online Access:https://revistas.inia.es/index.php/sjar/article/view/15228

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