ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2012
Genome-enabled prediction of genetic values using radial basis function neural networks.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 101 papers.
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Who cites it
101 citing papers in PubMed.
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Feed-forward neural networks for genomic selection: insights into varying levels of dominance heritability.BMC genomics · 2026Article
- Enhancing genomic prediction ability of blast resistance using genome-wide association study-derived marker weights in two rice (Oryza sativa L.) populations.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Genomic prediction of feed efficiency in boars by deep learning.G3 (Bethesda, Md.) · 2026Article
- Integrating Artificial Intelligence and Biotechnology to Enhance Cold Stress Resilience in Legumes.Plants (Basel, Switzerland) · 2025Review
- Comparing artificial and convolutional neural networks with traditional models for Genomic prediction in wheat.Molecular breeding : new strategies in plant improvement · 2025Article
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- GWAS-assisted and multitrait genomic prediction for improvement of seed yield and canning quality traits in a black bean breeding panel.G3 (Bethesda, Md.) · 2025Article
- Genomic Prediction for Germplasm Improvement Through Inter-Heterotic-Group Line Crossing in Maize.International journal of molecular sciences · 2025Article
- Big data and artificial intelligence-aided crop breeding: Progress and prospects.Journal of integrative plant biology · 2025Review
- Leveraging hierarchical structures for genetic block interaction studies using the hierarchical transformer.medRxiv : the preprint server for health sciences · 2025Article
- Integrative multi-environmental genomic prediction in apple.Horticulture research · 2025Article
- Review of applications of artificial intelligence (AI) methods in crop research.Journal of applied genetics · 2024Review
- Ability of Genomic Prediction to Bi-Parent-Derived Breeding Population Using Public Data for Soybean Oil and Protein Content.Plants (Basel, Switzerland) · 2024Article
- Optimizing drought tolerance in cassava through genomic selection.Frontiers in plant science · 2024Article
- Genome-wide association and genomic prediction for iron and zinc concentration and iron bioavailability in a collection of yellow dry beans.Frontiers in genetics · 2024Article
- Evaluation of eight Bayesian genomic prediction models for three micronutrient traits in bread wheat (Triticum aestivum L.).The plant genome · 2023Article
- Genomics for Yield and Yield Components in Durum Wheat.Plants (Basel, Switzerland) · 2023Review
- Multimodal deep learning methods enhance genomic prediction of wheat breeding.G3 (Bethesda, Md.) · 2023Article
- Results from rapid-cycle recurrent genomic selection in spring bread wheat.G3 (Bethesda, Md.) · 2023Article
41 more citing papers are in PubMed but not listed here.
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8 authors.
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Abstract
The availability of high density panels of molecular markers has prompted the adoption of genomic selection (GS) methods in animal and plant breeding. In GS, parametric, semi-parametric and non-parametric regressions models are used for predicting quantitative traits. This article shows how to use neural networks with radial basis functions (RBFs) for prediction with dense molecular markers. We illustrate the use of the linear Bayesian LASSO regression model and of two non-linear regression models, reproducing kernel Hilbert spaces (RKHS) regression and radial basis function neural networks (RBFNN) on simulated data and real maize lines genotyped with 55,000 markers and evaluated for several trait-environment combinations. The empirical results of this study indicated that the three models showed similar overall prediction accuracy, with a slight and consistent superiority of RKHS and RBFNN over the additive Bayesian LASSO model. Results from the simulated data indicate that RKHS and RBFNN models captured epistatic effects; however, adding non-signal (redundant) predictors (interaction between markers) can adversely affect the predictive accuracy of the non-linear regression models.
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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.