ReviewBMC genomics2021
A review of deep learning applications for genomic selection.
Review in BMC genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 177 papers, 3 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
177 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine learning for multi-omics data integration in crop improvement: a systematic review.BMC bioinformatics · 2026Pooled it
- Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017-2023.Frontiers in physiology · 2023Pooled it
- Genome-Enabled Prediction Methods Based on Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2022Pooled it
- Photosynthesis-informed breeding to enhance Soybean (Glycine max) yield potential: a framework integrating high-throughput phenomics, genomic prediction and G×E×M.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Multi-variant GWAS and genomic prediction dissect the genetic architecture underlying tomato fruit weight.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Enhancing Arabica and Robusta coffee prices forecasting through machine learning approaches.Scientific reports · 2026Article
- Evaluating Causal Claims in Plant Developmental Metabolism: A When-Where-How Framework.Plants (Basel, Switzerland) · 2026Review
- Integrating deep learning and pangenomics to recover missing heritability from wild structural variations.BMC genomics · 2026Review
- Calcium Homeostasis and Parturient Paresis in Ruminants: Mechanistic Insights and Clinical Management.Animals : an open access journal from MDPI · 2026Review
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- Integrating speed breeding and genomic tools to accelerate genetic gain in chilli (Capsicum annuum L.).Journal of applied genetics · 2026Review
- Robustly enhancing crop genomic prediction accuracy through ensemble learning and iterative optimization.Nature communications · 2026Article
- A non-linear game for two: genetic parameters and prediction of fertilization success using Bayesian and machine learning frameworks.Genetics, selection, evolution : GSE · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Article
- DNAwhisper: An Integrated Deep Learning Pyramidal Framework for Multi-Trait Genomic Prediction and Adaptive Marker Prioritisation.Plant biotechnology journal · 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
- ConvCGP: A convolutional neural network to predict genetic values of agronomic traits from compressed genome-wide polymorphisms.The plant genome · 2026Article
- Coformer: a deep learning-based framework for cross-environment and multi-year cotton phenotype prediction and interpretation.Plant methods · 2026Article
- An explainable AI framework integrating machine and deep learning models for multi-species DNA functional group classification.Scientific reports · 2026Article
117 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral 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 complicated associations between data and output with the ability to adapt to very complex patterns. MAIN BODY: We review the applications of deep learning (DL) methods in genomic selection (GS) to obtain a meta-picture of GS performance and highlight how these tools can help solve challenging plant breeding problems. We also provide general guidance for the effective use of DL methods including the fundamentals of DL and the requirements for its appropriate use. We discuss the pros and cons of this technique compared to traditional genomic prediction approaches as well as the current trends in DL applications.
conclusionsThe main requirement for using DL is the quality and sufficiently large training data. Although, based on current literature GS in plant and animal breeding we did not find clear superiority of DL in terms of prediction power compared to conventional genome based prediction models. Nevertheless, there are clear evidences that DL algorithms capture nonlinear patterns more efficiently than conventional genome based. Deep learning algorithms are able to integrate data from different sources as is usually needed in GS assisted breeding and it shows the ability for improving prediction accuracy for large plant breeding data. It is important to apply DL to large training-testing data sets.
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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.