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

A review of deep learning applications for genomic selection.

BMC genomics - 6 Jan 2021

Montesinos-López Osval Antonio, Montesinos-López Abelardo, Pérez-Rodríguez Paulino, Barrón-López José Alberto, Martini Johannes W R, Fajardo-Flores Silvia Berenice, Gaytan-Lugo Laura S, Santana-Mancilla Pedro C, Crossa José

Abstract excerpt

BACKGROUND: Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered in the context of genomic prediction. The DL methods are nonparametric models providing flexibility to adapt to...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Bayes Theorem
Share to Deep Learning

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genome

Share to Genomics

Share to Models, Genetic

Share to Phenotype

Share to Selection, Genetic

Source

Open in PubMed Open full text

Identifiers

PMID: 33407114
DOI: 10.1186/s12864-020-07319-x
PMCID: PMC7789712

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

A review of deep learning applications for genomic selection.PMID 33407114Read ↗

Multimodal deep learning methods enhance genomic prediction of wheat breeding.Semantic proximity Read ↗

Accounting for Correlation Between Traits in Genomic Prediction.Semantic proximity Read ↗

ReaGP: integrating residual units and attention mechanisms in convolution neural network for genomic prediction.Semantic proximity Read ↗

Deep learning for plant genomics and crop improvement.Semantic proximity Read ↗

Genomic selection in plant breeding: Key factors shaping two decades of progress.Semantic proximity Read ↗

CLCNet: a contrastive learning and chromosome-aware network for genomic prediction in plants.Semantic proximity Read ↗

ResDeepGS: A deep learning-based method for crop phenotype prediction.Semantic proximity Read ↗

deepGBLUP: joint deep learning networks and GBLUP framework for accurate genomic prediction of complex traits in Korean native cattle.Semantic proximity Read ↗

Genomic selection: Essence, applications, and prospects.Semantic proximity Read ↗

Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes.Semantic proximity Read ↗

Overview of Genomic Prediction Methods and the Associated Assumptions on the Variance of Marker Effect, and on the Architecture of the Target Trait.Semantic proximity Read ↗

A Comparison of Three Machine Learning Methods for Multivariate Genomic Prediction Using the Sparse Kernels Method (SKM) Library.Semantic proximity Read ↗

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