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Fig. 2 | Genome Biology

Fig. 2

From: KAML: improving genomic prediction accuracy of complex traits using machine learning determined parameters

Fig. 2

Comparison of prediction accuracy performances of LMM (red), BSLMM (yellow), BayesR (violet), and KAML (blue) in cattle, horse, and maize datasets. The prediction accuracy performance of each method was measured by the correlation method, which is the average Pearson correlation between predicted values and phenotypic values of 20 replicates in the validation subset. In each replicate, the dataset was randomly split into a reference subset containing 80% of individuals and a validation subset containing the remaining 20%. For each boxplot, the middle line represents the average value, the bottom and top are the standard deviation, and the upper and lower ends of each box represent the maximum and minimum, respectively

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