Plant genotype to phenotype prediction using machine learning
Genomic prediction tools support crop breeding based on statistical methods, such as the
genomic best linear unbiased prediction (GBLUP). However, these tools are not designed to …
genomic best linear unbiased prediction (GBLUP). However, these tools are not designed to …
A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species
Genomic selection is an integral tool for breeders to accurately select plants directly from
genotype data leading to faster and more resource-efficient breeding programs. Several …
genotype data leading to faster and more resource-efficient breeding programs. Several …
Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction
The first paradigm of plant breeding involves direct selection-based phenotypic observation,
followed by predictive breeding using statistical models for quantitative traits constructed …
followed by predictive breeding using statistical models for quantitative traits constructed …
MegaLMM: mega-scale linear mixed models for genomic predictions with thousands of traits
Large-scale phenotype data can enhance the power of genomic prediction in plant and
animal breeding, as well as human genetics. However, the statistical foundation of multi-trait …
animal breeding, as well as human genetics. However, the statistical foundation of multi-trait …
Benchmarking parametric and machine learning models for genomic prediction of complex traits
The usefulness of genomic prediction in crop and livestock breeding programs has
prompted efforts to develop new and improved genomic prediction algorithms, such as …
prompted efforts to develop new and improved genomic prediction algorithms, such as …
Optimizing genomic-enabled prediction in small-scale maize hybrid breeding programs: a roadmap review
R Fritsche-Neto, G Galli, KLR Borges… - Frontiers in Plant …, 2021 - frontiersin.org
The usefulness of genomic prediction (GP) for many animal and plant breeding programs
has been highlighted for many studies in the last 20 years. In maize breeding programs …
has been highlighted for many studies in the last 20 years. In maize breeding programs …
[HTML][HTML] Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data
H Tong, Z Nikoloski - Journal of plant physiology, 2021 - Elsevier
Highly efficient and accurate selection of elite genotypes can lead to dramatic shortening of
the breeding cycle in major crops relevant for sustaining present demands for food, feed …
the breeding cycle in major crops relevant for sustaining present demands for food, feed …
Machine learning for predicting phenotype from genotype and environment
Predicting phenotype with genomic and environmental information is critically needed and
challenging. Machine learning methods have emerged as powerful tools to make accurate …
challenging. Machine learning methods have emerged as powerful tools to make accurate …
Leveraging biological insight and environmental variation to improve phenotypic prediction: Integrating crop growth models (CGM) with whole genome prediction …
A successful strategy for prediction of crop yield that accounts for the effects of genotype,
environment and their interactions with management will create many opportunities for …
environment and their interactions with management will create many opportunities for …
Multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits
OA Montesinos-López… - G3: Genes, genomes …, 2018 - academic.oup.com
Multi-trait and multi-environment data are common in animal and plant breeding programs.
However, what is lacking are more powerful statistical models that can exploit the correlation …
However, what is lacking are more powerful statistical models that can exploit the correlation …