ArticleFrontiers in plant science2022
A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Integrating multi-layer perceptron and random forest in an ensemble framework for improved genomic prediction accuracy and SHAP-derived interpretability of residual feed intake in cattle.Journal of animal science and biotechnology · 2026Article
- Ensemble-based genomic prediction for maize flowering time improves prediction accuracy and reveals novel insights into trait genetic variation.G3 (Bethesda, Md.) · 2026Article
- Identification of biogeographically informative microssatelite markers for Brazilian Cannabis sativa samples: a machine learning approach for forensic origin prediction.International journal of legal medicine · 2026Article
- Machine learning and mathematical modeling for comparative analysis of green-synthesized ZnO nanoparticles as seed nano-priming agents for linseed.Frontiers in plant science · 2026Article
- Haplotype-based autoencoders can reduce the dataset dimension and estimate haplotype block effects in different crop species.BMC bioinformatics · 2025Article
- Semi-parametric validation of genomic predictions and polygenic risk scores with the Blupf90 software suite.G3 (Bethesda, Md.) · 2025Article
- Determination of optimal sampling time of grape embryo rescue based on near infrared spectroscopy combined with machine learning.Plant phenomics (Washington, D.C.) · 2025Article
- Improved genomic prediction performance with ensembles of diverse models.G3 (Bethesda, Md.) · 2025Article
- Distinguishing isotropic and anisotropic signals for X-ray total scattering using machine learning.Acta crystallographica. Section A, Foundations and advances · 2025Article
- Enhancing Genomic Prediction Accuracy of Reproduction Traits in Rongchang Pigs Through Machine Learning.Animals : an open access journal from MDPI · 2025Article
- Applying traditional and machine learning-based GWAS approaches for marker-trait identification in wheat.Frontiers in plant science · 2025Article
- Obscured-ensemble models for genomic prediction.PloS one · 2025Article
- Review of applications of artificial intelligence (AI) methods in crop research.Journal of applied genetics · 2024Review
- Explainable artificial intelligence for genotype-to-phenotype prediction in plant breeding: a case study with a dataset from an almond germplasm collection.Frontiers in plant science · 2024Article
- A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions.Frontiers in plant science · 2024Review
- PopGenAdapt: Semi-Supervised Domain Adaptation for Genotype-to-Phenotype Prediction in Underrepresented Populations.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- PopGenAdapt: Semi-Supervised Domain Adaptation for Genotype-to-Phenotype Prediction in Underrepresented Populations.bioRxiv : the preprint server for biology · 2023Article
- Advancing artificial intelligence to help feed the world.Nature biotechnology · 2023Article
- easyPheno: An easy-to-use and easy-to-extend Python framework for phenotype prediction using Bayesian optimization.Bioinformatics advances · 2023Article
- Comparing feature selection and machine learning approaches for predictingFrontiers in neuroinformatics · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 prediction methods have been established in the last few years. These range from classical linear mixed models to complex non-linear machine learning approaches, such as Support Vector Regression, and modern deep learning-based architectures. Many of these methods have been extensively evaluated on different crop species with varying outcomes. In this work, our aim is to systematically compare 12 different phenotype prediction models, including basic genomic selection methods to more advanced deep learning-based techniques. More importantly, we assess the performance of these models on simulated phenotype data as well as on real-world data from
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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 Socratic graph.