Table 5 Results on the Korean native cattle data with different combinations of deepGBLUP components: (1) Deep learning networks \(\hat{{\textbf{b}}}_\text {deep}\), (2) additive GBLUP \(\hat{{\textbf{b}}}_\text {a}\), (3) dominance GBLUP \(\hat{{\textbf{b}}}_\text {d}\), (4) epistasis GBLUP \(\hat{{\textbf{b}}}_\text {e}\)
Component | CWT | BF | EMA | MS | |||
|---|---|---|---|---|---|---|---|
\(\hat{{\textbf{b}}}_\text {deep}\) | \(\hat{{\textbf{b}}}_\text {a}\) | \(\hat{{\textbf{b}}}_\text {d}\) | \(\hat{{\textbf{b}}}_\text {e}\) | ||||
✓ | 0.746 ± 0.017 | 0.661 ± 0.009 | 0.722 ± 0.014 | 0.622 ± 0.011 | |||
✓ | ✓ | 0.753 ± 0.015 | 0.673 ± 0.009 | 0.744 ± 0.016 | 0.666 ± 0.012 | ||
✓ | ✓ | 0.748 ± 0.017 | 0.659 ± 0.01 | 0.725 ± 0.014 | 0.623 ± 0.011 | ||
✓ | ✓ | 0.747 ± 0.016 | 0.671 ± 0.009 | 0.734 ± 0.016 | 0.646 ± 0.012 | ||
✓ | ✓ | ✓ | 0.755 ± 0.016 | 0.672 ± 0.009 | 0.746 ± 0.016 | 0.666 ± 0.012 | |
✓ | ✓ | ✓ | 0.751 ± 0.015 | 0.673 ± 0.009 | 0.744 ± 0.017 | 0.672 ± 0.012 | |
✓ | ✓ | ✓ | 0.748 ± 0.016 | 0.669 ± 0.009 | 0.736 ± 0.016 | \(\underline{0.647 \pm 0.011}\) | |
✓ | ✓ | ✓ | \(\underline{0.725 \pm 0.016}\) | \(\underline{0.639 \pm 0.01}\) | \(\underline{0.722 \pm 0.019}\) | 0.665 ± 0.014 | |
✓ | ✓ | ✓ | ✓ | 0.752 ± 0.016 | 0.673 ± 0.009 | 0.746 ± 0.017 | 0.672 ± 0.012 |