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Table 5 Prediction performance in terms of Pearson’s correlation for the simulated and real Arabidopsis datasets (Pook et al., 2020)

From: A review of deep learning applications for genomic selection

A). Predictive ability on different traits with      
Trait architecture GBLUP BayesA EGBLUP MPL CNN LCNN
10 additive QTL 0.639 0.66 0.635 0.637 0.627 0.666
1000 additive QTL 0.516 0.538 0.543 0.524 0.538 0.606
10 epistatic QTL 0.511 0.527 0.519 0.503 0.491 0.572
1000 epistatic QTL 0.416 0.414 0.448 0.395 0.403 0.401
10 locally linked epistatic QTL 0.488 0.501 0.529 0.504 0.544 0.625
1000 locally linked epistatic QTL 0.524 0.523 0.541 0.519 0.517 0.51
B). Predictive ability for the Arabidopsis traits
 Trait architecture GBLUP BayesA EGBLUP MLP CNN LCNN
 Average predictive ability (all) 0.39 0.382 0.382 0.316 0.312 0.34
 Average predictive ability (training set < 100) 0.404 0.39 0.399 0.3 0.299 0.326
 Average predictive ability (100 < training set < 250) 0.364 0.358 0.354 0.318 0.311 0.327
 Average predictive ability (training set > 250) 0.477 0.477 0.472 0.358 0.37 0.456