ArticleG3 (Bethesda, Md.)2019
Benchmarking Parametric and Machine Learning Models for Genomic Prediction of Complex Traits.
Article in G3 (Bethesda, Md.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 150 papers, 2 of them syntheses that pooled it.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
150 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning for multi-omics data integration in crop improvement: a systematic review.BMC bioinformatics · 2026Pooled it
- Fast analysis of biobank-size data and meta-analysis using the BGLR R-package.G3 (Bethesda, Md.) · 2025Pooled it
- Mul-PheG2P: decoupled learning and prediction-space fusion enables robust and interpretable multi-phenotype genomic prediction.The New phytologist · 2026Article
- BiMSGP: A Bidirectional Mamba-Based Model for Scalable and Accurate Genomic Prediction in Plants.Genes · 2026Article
- Genomic prediction in quinoa across contrasting environments using statistical and machine learning models.The plant genome · 2026Article
- Predictive models of the genetic bases underlying budding yeast fitness in multiple environments.NAR genomics and bioinformatics · 2026Article
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- Highly Polygenic Control of Photosynthetic Responses to Nighttime Temperature Studied by Genomic Prediction.Plant, cell & environment · 2026Article
- Evolutionary structure constrains genomic prediction accuracy more than model complexity in mango (Mangifera indica L.).G3 (Bethesda, Md.) · 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
- OptimGS: a dual integrative genomic prediction framework for improving cold stress tolerance in wheat.Briefings in bioinformatics · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Local SNP-explained methylation variation reveals genetically anchored and exposure-associated methylation architecture in the human brain.bioRxiv : the preprint server for biology · 2026Article
- Ensemble-based genomic prediction for maize flowering time improves prediction accuracy and reveals novel insights into trait genetic variation.G3 (Bethesda, Md.) · 2026Article
- Image-based high-throughput phenotyping enables genetic analyses of pod morphological traits in mungbean (Vigna radiata (L.) R. Wilczek).G3 (Bethesda, Md.) · 2026Article
- Predicting complex phenotypes using multi-omics data in maize.The Plant cell · 2026Article
- Multi-environment evaluation and genomic prediction of agronomic traits in the southern US rice genepool.The plant genome · 2026Article
- A benchmarking of genomic selection models for predicting grain-yield related traits using haplotype-based and genome-wide association study-based markers in rice.The plant genome · 2026Article
- 2NPLGBM: a genomic model that merges the strengths of classical and machine learning methods in genomic prediction.Plant methods · 2026Article
- CWAGS: multi-trait genomic selection using channel weighted attention convolutional network.BMC genomics · 2026Article
90 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The usefulness of genomic prediction in crop and livestock breeding programs has prompted efforts to develop new and improved genomic prediction algorithms, such as artificial neural networks and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of datasets and models. Using data of 18 traits across six plant species with different marker densities and training population sizes, we compared the performance of six linear and six non-linear algorithms. First, we found that hyperparameter selection was necessary for all non-linear algorithms and that feature selection prior to model training was critical for artificial neural networks when the markers greatly outnumbered the number of training lines. Across all species and trait combinations, no one algorithm performed best, however predictions based on a combination of results from multiple algorithms (
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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.