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Table 3 Prediction performance in terms of root mean square error of prediction (RMSE) of the four models (MLP_20, LR, MLP_1, RT) reported by Khaki and Wang (2019); A) in a maize dataset. MPL_20 denotes the MLP model with 20 hidden layers, and MPL_1 denotes the MLP model with 1 hidden layer. Prediction performance in terms of Pearson’s correlation of 6 species across traits evaluated with 11 methods (Azodi et al., 2019); B). SVR denotes support vector regression. SVR_lin denotes SVR with linear kernel, SVR_poly denotes SVR with polynomial kernel, SVR_rbf denotes SVR with kernel Radial Basis Function

From: A review of deep learning applications for genomic selection

A   Model Trait RMSE   
   MLP_20 Yield 12.79   
    Check yield 11.38   
    Yield difference 12.4   
   LR Yield 21.4   
    Check yield 19.87   
    Yield difference 13.11   
   MLP_1 Yield 18.04   
    Check yield 15.18   
    Yield difference 15.19   
   RT Yield 15.03   
    Check yield 14.87   
    Yield difference 15.92   
B Method Maize Rice Sorghum Soy Spruce Switch-grass
  rrBLUP 0.44 0.34 0.63 0.46 0.32 0.61
  BRR 0.44 0.39 0.63 0.46 0.32 0.61
  BayesA 0.42 0.38 0.63 0.47 0.32 0.61
  BayesB 0.43 0.38 0.63 0.46 0.32 0.61
  BL 0.44 0.39 0.62 0.46 0.32 0.61
  SVR_lin 0.41 0.38 0.62 0.43 0.19 0.6
  SVR_poly 0.43 0.38 0.63 0.41 0.33 0.61
  SVR_rbf 0.39 0.38 0.63 0.04 0.34 0.6
  RF 0.43 0.4 0.58 0.36 0.35 0.57
  GTB 0.37 0.38 0.58 0.4 0.33 0.56
  MLP 0.17 0.08 0.45 0.44 0.28 0.45