ArticleFrontiers in plant science2021
Prediction of Maize Phenotypic Traits With Genomic and Environmental Predictors Using Gradient Boosting Frameworks.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 61 citations in OpenAlex.
- Structured multimodal deep learning improves genomic prediction in future environments.Genetics · 2026Article
- Yield-graph: multi-stage growth-aware maize yield prediction via graph neural networks.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Advances in root phenotyping: high-throughput imaging, computational tools, and integrative approaches for crop improvement.Journal of experimental botany · 2026Review
- Genomic prediction in quinoa across contrasting environments using statistical and machine learning models.The plant genome · 2026Article
- Review
- Enhancing alfalfa breeding through genomic prediction with exotic germplasm resources.BMC plant biology · 2026Article
- Tailoring AI and ML models for genotype-by-environment prediction leveraging environmental covariates: A European rye example.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Multi-metric evaluation and parametric optimization of stochastic gradient boosting machines for genomic prediction and selection in wheat (Triticum aestivum) breeding.G3 (Bethesda, Md.) · 2026Article
- 2NPLGBM: a genomic model that merges the strengths of classical and machine learning methods in genomic prediction.Plant methods · 2026Article
- LSTM-attention-guided graph neural networks for integrated genotype-Environment modeling in maize yield prediction.PLoS computational biology · 2026Article
- Critical evaluation of the theory and practice of feed-forward neural networks for genomic prediction.G3 (Bethesda, Md.) · 2026Article
- MaizeField3D: A curated 3D point cloud and procedural model dataset of field-grown maize from a diversity panel.Plant phenomics (Washington, D.C.) · 2026Article
- A machine learning framework reveals key drivers of cytokine responses in a healthy human cohort.NPJ systems biology and applications · 2026Article
- Responses and adaptations of plants to abiotic stress: transcriptional regulation of secondary metabolic pathways, metabolomics, and nanobiological approaches.Frontiers in genetics · 2026Review
- Predicting maize hybrid performance with machine learning and a locus-specific weighted degree of dominance transformation.Frontiers in plant science · 2026Article
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- Disentangling soybean GxE effects in an integrated genomic prediction and machine learning-GWAS workflow.Plant methods · 2025Article
- Integration of crop modeling and sensing into molecular breeding for nutritional quality and stress tolerance.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Improving plant breeding through AI-supported data integration.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Environment ensemble models for genomic prediction in common bean (Phaseolus vulgaris L.).The plant genome · 2025Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of crop varieties with stable performance in future environmental conditions represents a critical challenge in the context of climate change. Environmental data collected at the field level, such as soil and climatic information, can be relevant to improve predictive ability in genomic prediction models by describing more precisely genotype-by-environment interactions, which represent a key component of the phenotypic response for complex crop agronomic traits. Modern predictive modeling approaches can efficiently handle various data types and are able to capture complex nonlinear relationships in large datasets. In particular, machine learning techniques have gained substantial interest in recent years. Here we examined the predictive ability of machine learning-based models for two phenotypic traits in maize using data collected by the Maize Genomes to Fields (G2F) Initiative. The data we analyzed consisted of multi-environment trials (METs) dispersed across the United States and Canada from 2014 to 2017. An assortment of soil- and weather-related variables was derived and used in prediction models alongside genotypic data. Linear random effects models were compared to a linear regularized regression method (
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.