Additional file 5 of Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data
Creators: name: Lourenço, Vanda M.
name: Ogutu, Joseph O.
name: Rodrigues, Rui A.P.
name: Posekany, Alexandra
name: Piepho, Hans-Peter
Publisher: name: figshare
Year: 2024
Subjects: subject: Artificial Intelligence and Image Processing
subject: FOS: Computer and information sciences

description: Additional file 5. Includes the RR-BLUP model used to estimate variance components for the KWS real maize data (Section 1), the Noteworthy details of model fitting (Section 2) plus the additional Tables of results (Section 3). Table S1. List of R and Python packages used in this paper. Table S2. Prediction accuracy (PA) of the regularized, adaptive regularized and Bayesian regularized methods, computed as the Pearson correlation coefficient between the true breeding values (TBVs) and the predicted breeding values (PBVs), for the simulated dataset, where $$T_1-T_3$$ T 1 - T 3 refer to three quantitative milk traits. The choice of $$\lambda$$ λ , where applicable, was based on the 10-fold CV. The mean squared and absolute prediction errors are also provided. Table S3. Prediction accuracy (PA) of the group regularized methods (mean and range values of PA across the different groupings), computed as the Pearson correlation coefficient between the true breeding values (TBVs) and the predicted breeding values (PBVs), for the simulated dataset, where $$T_1-T_3$$ T 1 - T 3 refer to three quantitative milk traits. Choice of $$\lambda$$ λ was based on the 10-fold CV. Display refers to the mean, max and min values of PA across all the 10 grouping schemes. The mean squared and absolute prediction errors are also provided. Table S4. Prediction accuracy (PA) of the ensemble and instance-based methods, computed as the Pearson correlation coefficient between the true breeding values (TBVs) and the predicted breeding values (PBVs), for the simulated dataset, where $$T_1-T_3$$ T 1 - T 3 refer to three quantitative milk traits. Table S5. Prediction accuracy (PA) of the deep learning methods, computed as the Pearson correlation coefficient between the true breeding values (TBVs) and the predicted breeding values (PBVs), for the simulated dataset, where $$T_1-T_3$$ T 1 - T 3 refer to three quantitative milk traits. Table S6. Best FFNN model calibration parameters selected for each of the three quantitative milk traits $$T_1-T_3$$ T 1 - T 3 . Table S7. Best CNN model calibration parameters (Number of epochs/Learning rate) selected for each of the three quantitative milk traits $$T_1-T_3$$ T 1 - T 3 . Table S8. Predictive ability (PA; mean and range values computed across the 5-fold validation datasets and 10 replicates) of the regularized, adaptive regularized, group regularized, Bayesian regularized, ensemble, instance-based and deep learning methods, computed as the Pearson correlation coefficient between the observed breeding values (OBVs) and the predicted breeding values (PBVs), for the KWS datasets. The choice of $$\lambda$$ λ , where applicable, was based on 4-fold CV.