Residual networks without pooling layers improve the accuracy of genomic predictions-Reference-Cited by-同舟云学术

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Residual networks without pooling layers improve the accuracy of genomic predictions

Published:2024-05-21 Issue:6 Volume:137 Page:
ISSN:0040-5752
Container-title:Theoretical and Applied Genetics
language:en
Short-container-title:Theor Appl Genet

Author:

Xie Zhengchao^ORCID,Xu Xiaogang^ORCID,Li Ling,Wu Cuiling,Ma Yinxing,He Jingjing,Wei Sidi,Wang Jun,Feng Xianzhong

Funder

Innovative Research Group Project of the National Natural Science Foundation of China

Soybean Intelligent Computational Breeding and Application of Zhejiang Lab

Intelligent Technology and Platform Development for Rice Breeding of Zhejiang Lab

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00122-024-04649-2.pdf

Reference65 articles.

1. Abdollahi-Arpanahi R, Gianola D, Peñagaricano F (2020) Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes. Genet Sel Evol 52:1–15

2. Alwosheel A, van Cranenburgh S, Chorus CG (2018) Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis. J Choice Model 28:167–182

3. Amin J, Sharif M, Anjum MA (2020) Convolutional neural network with batch normalization for glioma and stroke lesion detection using MRI. Cogn Syst Res 59:304–311

4. Annicchiarico P, Nazzicari N, Li X (2015) Accuracy of genomic selection for alfalfa biomass yield in different reference populations. BMC Genomics 16:1–13

5. Bradbury PJ, Zhang Z, Kroon DE (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635

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