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Table 8 The maximum and minimum difference of the predictive ability between ELPGV and other methods in different sample size of simulation

From: Ensemble learning for integrative prediction of genetic values with genomic variants

Method

100

200

300

400

500

599

Predictive ability

Predictive ability

Predictive ability

Predictive ability

Predictive ability

Predictive ability

ELPGV

0.6055 ± 0.0220

0.7175 ± 0.0159

0.7559 ± 0.0085

0.7189 ± 0.0070

0.7378 ± 0.0061

0.7034 ± 0.0064

BayesA

0.5486 ± 0.0256

0.6827 ± 0.0170

0.7200 ± 0.0085

0.7034 ± 0.0073

0.7231 ± 0.0069

0.6865 ± 0.0068

BayesB

0.6007 ± 0.0237

0.7075 ± 0.0170

0.7502 ± 0.0084

0.7158 ± 0.0069

0.7344 ± 0.0060

0.6985 ± 0.0065

BayesCÏ€

0.5334 ± 0.0215

0.6215 ± 0.0266

0.7271 ± 0.0100

0.7069 ± 0.0076

0.7354 ± 0.0060

0.6955 ± 0.0064

GBLUP

0.5266 ± 0.0216

0.4785 ± 0.0243

0.5655 ± 0.0151

0.5511 ± 0.0143

0.5934 ± 0.0095

0.5505 ± 0.0096

Maximum difference

0.0789 (15.0%)

0.2390 (49.9%)

0.1904 (33.7%)

0.1678 (30.4%)

0.1444 (24.3%)

0.1529 (27.7%)

Minimum difference

0.0048 (0.8%)

0.0100 (1.4%)

0.0057 (0.8%)

0.0031 (0.4%)

0.0024 (0.3%)

0.0049 (0.7%)

  1. ELPGV is the ensemble learning based on BayesA, BayesB, BayesCÏ€ and GBLUP