Research graph
References from Structured multimodal deep learning improves genomic prediction in future environments. Local targets link to admitted publications; unresolved targets remain external evidence.
Performance prediction of crosses in plant breeding through genotype by environment interactions
10.1038/s41598-020-68343-1 · 2020 · External reference
Unresolved reference
2016 · External reference
Uniprot: the universal protein knowledgebase in 2025
10.1093/nar/gkae1010 · 2025 · External reference
Unresolved reference
2019 · External reference
Plant adaptation to climate change–opportunities and priorities in breeding
10.1071/cp11303 · 2012 · External reference
Unresolved reference
2016 · External reference
Extending the breeder’s equation to take aim at the target population of environments
10.3389/fpls.2023.1129591 · 2023 · External reference
Use of crop growth models with whole-genome prediction: application to a maize multienvironment trial
10.2135/cropsci2015.08.0512 · 2016 · External reference
Genome-enabled prediction for sparse testing in multi-environmental wheat trials
10.1002/tpg2.20151 · 2021 · External reference
Unresolved reference
2023 · External reference
Unresolved reference
2023 · External reference
Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials
10.1007/s00122-024-04687-w · 2024 · External reference
Unresolved reference
2016 · External reference
Unresolved reference
2017 · External reference
Multi-trait genomic prediction of yield-related traits in us soft wheat under variable water regimes
10.3390/genes11111270 · 2020 · External reference
Leveraging automated machine learning for environmental data-driven genetic analysis and genomic prediction in maize hybrids
10.1002/advs.202412423 · 2025 · External reference
Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions
10.1007/s00122-013-2231-5 · 2014 · External reference
Unresolved reference
2020 · External reference
Unresolved reference
2017 · External reference
Don’t blup twice
10.1093/g3journal/jkae250 · 2024 · External reference
Megalmm improves genomic predictions in new environments using environmental covariates
10.1093/genetics/iyae171 · 2025 · External reference
Unresolved reference
2022 · External reference
Agritransformer: a transformer-based model with attention mechanisms for enhanced multimodal crop yield prediction
10.3390/electronics14122466 · 2025 · External reference
Explaining explanations: axiomatic feature interactions for deep networks
2021 · External reference
A reaction norm model for genomic selection using high-dimensional genomic and environmental data
10.1007/s00122-013-2243-1 · 2014 · External reference
Genomic prediction enhanced sparse testing for multi-environment trials
10.1534/g3.120.401349 · 2020 · External reference
Crop genomic selection with deep learning and environmental data: a survey
10.3389/frai.2022.1040295 · 2023 · External reference
Gxenet: novel fully connected neural network based approaches to incorporate gxe for predicting wheat yield
10.1016/j.aiia.2023.05.001 · 2023 · External reference
Characterization of the interactions between architecture and source–sink relationships in winter oilseed rape (brassica napus) using the greenlab model
10.1093/aob/mcq205 · 2011 · External reference
Climate change impacts on crop yield, crop water productivity and food security–a review
10.1016/j.pnsc.2009.08.001 · 2009 · External reference
Crop yield prediction using deep neural networks
10.3389/fpls.2019.00621 · 2019 · External reference
A cnn-rnn framework for crop yield prediction
10.3389/fpls.2019.01750 · 2020 · External reference
Yield prediction through integration of genetic, environment, and management data through deep learning
10.1093/g3journal/jkad006 · 2023 · External reference
Idea factory: the maize genomes to fields initiative
10.2135/cropsci2019.02.0071 · 2019 · External reference
Genomes to fields 2022 maize genotype by environment prediction competition
10.1186/s13104-023-06421-z · 2023 · External reference
Phenotype prediction and genome-wide association study using deep convolutional neural network of soybean
10.3389/fgene.2019.01091 · 2019 · External reference
Unresolved reference
2019 · External reference
Unresolved reference
2017 · External reference
A deep convolutional neural network approach for predicting phenotypes from genotypes
10.1007/s00425-018-2976-9 · 2018 · External reference
Detecting incidental correlation in multimodal learning via latent variable modeling
2023 · External reference
Multimodal performers for genomic selection and crop yield prediction
10.1016/j.atech.2021.100017 · 2021 · External reference
Unresolved reference
2023 · External reference
Prediction of total genetic value using genome-wide dense marker maps
10.1093/genetics/157.4.1819 · 2001 · External reference
Multimodal deep learning methods enhance genomic prediction of wheat breeding
10.1093/g3journal/jkad045 · 2023 · External reference
Multi-environment genomic prediction of plant traits using deep learners with dense architecture
10.1534/g3.118.200740 · 2018 · External reference
Multi-trait, multi-environment genomic prediction of durum wheat with genomic best linear unbiased predictor and deep learning methods
10.3389/fpls.2019.01311 · 2019 · External reference
A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding
10.1093/genetics/iyae161 · 2024 · External reference
Unresolved reference
2010 · External reference
Unresolved reference
2011 · External reference
Unresolved reference
2022 · External reference
Factor-analytic variance–covariance structures for prediction into a target population of environments
10.1002/bimj.202400008 · 2024 · External reference
Using crop growth model stress covariates and ammi decomposition to better predict genotype-by-environment interactions
10.1007/s00122-019-03432-y · 2019 · External reference
Correcting for spatial heterogeneity in plant breeding experiments with p-splines
10.1016/j.spasta.2017.10.003 · 2017 · External reference
The perceptron: a probabilistic model for information storage and organization in the brain
10.1037/h0042519 · 1958 · External reference
Multitrait machine-and deep-learning models for genomic selection using spectral information in a wheat breeding program
10.1002/tpg2.20119 · 2021 · External reference
Crop yield prediction integrating genotype and weather variables using deep learning
10.1371/journal.pone.0252402 · 2021 · External reference
Dropout: a simple way to prevent neural networks from overfitting
2014 · External reference
Integrating crop growth models with whole genome prediction through approximate bayesian computation
10.1371/journal.pone.0130855 · 2015 · External reference
Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics
10.1093/bioinformatics/btad336 · 2023 · External reference
Modelling spatial trends in sorghum breeding field trials using a two-dimensional p-spline mixed model
10.1007/s00122-017-2894-4 · 2017 · External reference
Cropformer: an interpretable deep learning framework for crop genomic prediction
10.1016/j.xplc.2024.101223 · 2025 · External reference
Dnngp, a deep neural network-based method for genomic prediction using multi-omics data in plants
10.1016/j.molp.2022.11.004 · 2023 · External reference
Unresolved reference
2021 · External reference
Unresolved reference
2020 · External reference
Predicting phenotypes from genetic, environment, management, and historical data using cnns
10.1007/s00122-021-03943-7 · 2021 · External reference
Global genotype by environment prediction competition reveals that diverse modeling strategies can deliver satisfactory maize yield estimates
10.1093/genetics/iyae195 · 2025 · External reference
Unresolved reference
2025 · External reference
Unresolved reference
2024 · External reference
Unresolved reference
2024 · External reference
Prediction of maize phenotypic traits with genomic and environmental predictors using gradient boosting frameworks
10.3389/fpls.2021.699589 · 2021 · External reference
Unresolved reference
2022 · External reference
Unresolved reference
2022 · External reference
Envirotyping for deciphering environmental impacts on crop plants
10.1007/s00122-016-2691-5 · 2016 · External reference
Geformer: a genotype-environment interaction-based genomic prediction method that integrates the gating multilayer perceptron and linear attention mechanisms
10.1016/j.molp.2025.01.020 · 2025 · External reference
Maizegep: a maize hybrids dataset with genotype, phenotype, and envirotype to develop genomic selection models
10.1093/gpbjnl/qzaf140 · 2026 · External reference
Unresolved reference
2024 · External reference
Unresolved reference
2024 · External reference
Extend mixed models to multilayer neural networks for genomic prediction including intermediate omics data
10.1093/genetics/iyac034 · 2022 · External reference
