ReviewFrontiers in genetics2022
Plant Genotype to Phenotype Prediction Using Machine Learning.
Review in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
42 citing papers in PubMed.
- Improving grain yield prediction in Southern US oat germplasm using genomics information and environmental covariates.The plant genome · 2026Article
- BiMSGP: A Bidirectional Mamba-Based Model for Scalable and Accurate Genomic Prediction in Plants.Genes · 2026Article
- Systems-Level Developmental Reprogramming Under Waterlogging Stress in Cowpea Revealed by Integrated Phenotypic, Physiological, and Transcriptomic Analysis.Plant, cell & environment · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Leveraging AI and integrated genomic-enviromic prediction for intelligent sugarcane breeding.Plant communications · 2026Review
- The emerging impact of CRISPR and gene editing on global crop improvement.Transgenic research · 2026Review
- Haplotype applications in genomic selection.Genome biology · 2026Review
- Explainable growth stage classification of cacao (Frontiers in plant science · 2026Article
- Partially connected neural networks for complex trait prediction: application to human height.Human genetics · 2025Article
- Normalized cumulative gain as an alternative evaluation measure for genomic selection models.Genetics, selection, evolution : GSE · 2025Article
- Microclimate-Controlled Smart Growth Cabinets for High-Throughput Plant Phenotyping.Sensors (Basel, Switzerland) · 2025Article
- Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP): Computational tool for interpreting ensembles of genomic prediction models.The plant genome · 2025Article
- Improving Genomic Prediction Using High-Dimensional Secondary Phenotypes: The Genetic Latent Factor Approach.Biometrical journal. Biometrische Zeitschrift · 2025Article
- AdaptiveGS: an explainable genomic selection framework based on adaptive stacking ensemble machine learning.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- MDNN: memetic deep neural network for genomic prediction.Briefings in bioinformatics · 2025Article
- Integrating multi-omics and machine learning for disease resistance prediction in legumes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- WheatGP, a genomic prediction method based on CNN and LSTM.Briefings in bioinformatics · 2025Article
- Genomic and phenomic prediction for soybean seed yield, protein, and oil.The plant genome · 2025Article
- Enhancing Genomic Prediction Accuracy of Reproduction Traits in Rongchang Pigs Through Machine Learning.Animals : an open access journal from MDPI · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 capture non-linear relationships within multi-dimensional datasets, or deal with high dimension datasets such as imagery collected by unmanned aerial vehicles. Machine learning (ML) algorithms have the potential to surpass the prediction accuracy of current tools used for genotype to phenotype prediction, due to their capacity to autonomously extract data features and represent their relationships at multiple levels of abstraction. This review addresses the challenges of applying statistical and machine learning methods for predicting phenotypic traits based on genetic markers, environment data, and imagery for crop breeding. We present the advantages and disadvantages of explainable model structures, discuss the potential of machine learning models for genotype to phenotype prediction in crop breeding, and the challenges, including the scarcity of high-quality datasets, inconsistent metadata annotation and the requirements of ML models.
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Registered trials
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.