Critical evaluation of the theory and practice of feed-forward neural networks for genomic prediction.

Abstract excerpt

Genomic prediction (GP) has catalyzed increased rates of genetic gain in animal and plant breeding. Recently, deep learning (DL) has been explored to increase GP accuracy by incorporating diverse data types and learning complex, non-linear patterns in datasets. However, DL consistently fails to significantly improve prediction accuracy over gold standard genomic BLUP (gBLUP) models. In this study, we first review...

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