Article
Residual networks without pooling layers improve the accuracy of genomic predictions.
TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik - 21 May 2024
Xie Zhengchao, Xu Xiaogang, Li Ling, Wu Cuiling, Ma Yinxing, He Jingjing, Wei Sidi, Wang Jun, Feng Xianzhong
Abstract excerpt
KEY MESSAGE: Residual neural network genomic selection is the first GS algorithm to reach 35 layers, and its prediction accuracy surpasses previous algorithms. With the decrease in DNA sequencing costs and the development of deep learning, phenotype prediction accuracy by genomic selection (GS) continues to improve. Residual networks, a widely validated deep learning technique, are introduced to deep learning for...
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