402. Machine learning and genetic improvement of animals and plants: where are we?
Author:
Affiliation:
1. University of Wisconsin-Madison, WI 53706, USA.
2. CIMMYT, 56237 Texcoco, Mexico.
3. Instituto Nacional de Investigaciones Agrarias, INIA-CSIC, 28040 Madrid, Spain.
Publisher
Wageningen Academic Publishers
Link
https://www.wageningenacademic.com/doi/pdf/10.3920/978-90-8686-940-4_402
Reference4 articles.
1. Multi-environment Genomic Prediction of Plant Traits Using Deep Learners With Dense Architecture
2. Technical Note: An R package for fitting Bayesian regularized neural networks with applications in animal breeding1
3. Reinoso-Peláez, E. L., Gianola, D., y González-Recio, O. (2022). Genome-enabled prediction methods based on machinelearning, in Genomic prediction of complex traits. Springer.
4. Interpretable artificial neural networks incorporating Bayesian alphabet models for genome-wide prediction and association studies
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1. Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non‐additive variation for key traits;The Plant Genome;2023-09-20
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