Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Non-linear kernel methods for genomic prediction of soybean yield and quality in multi-environment trials.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik - 23 Sept 2026

Paiva Wanessa Alves Lima, da Costa Weverton Gomes, Machado Leandro Pacheco, Nascimento Ana Carolina Campana, Jarquin Diego, Nascimento Moyses

Abstract excerpt

KEY MESSAGE: Nonlinear kernels improve the accuracy of genomic prediction while preserving the inferential framework of quantitative genetics. Improving the predictive accuracy of genomic prediction (GP) for complex genetic architectures involving non-additive effects and genotype-by-environment interactions (GEI) requires alternative approaches beyond conventional linear models such as Genomic Best Linear...

Read the complete abstract on PubMed

Topics

Glycine max
Models, Genetic
Gene-Environment Interaction
Genotype
Genomics
Prediction Algorithms

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Random Forest

Phenotype

Plant Breeding

Machine Learning

Source

Open in PubMed Open full text

Identifiers

PMID: 42776271
DOI: 10.1007/s00122-026-05387-3
PMCID: PMC13601130

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 19 Oct 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Non-linear kernel methods for genomic prediction of soybean yield and quality in multi-environment trials.PMID 42776271Read ↗

Nonlinear genomic selection index accelerates multi-trait crop improvement.Semantic proximity Read ↗

Quantitative Genomic Dissection of Soybean Yield Components.Semantic proximity Read ↗

GWKBR: a novel method integrating machine learning and Bayesian inference framework to improve genomic prediction accuracy.Semantic proximity Read ↗

Impact of environmental covariates summarization on predictive ability in genomic selection.Semantic proximity Read ↗

Near-infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments.Semantic proximity Read ↗

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.Semantic proximity Read ↗

Weighted kernels improve multi-environment genomic prediction.Semantic proximity Read ↗

Multi-trait multi-environment genomic prediction of preliminary yield trial in pulse crop.Semantic proximity Read ↗

Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials.Semantic proximity Read ↗

Integrative machine learning approach for identifying genes associated with quantitative traits: A soybean (Glycine max) yield case study.Semantic proximity Read ↗

Genomic prediction vs. gene-based crop models: a case study on rice trait prediction.Semantic proximity Read ↗

Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.