ArticleG3 (Bethesda, Md.)2019
New Deep Learning Genomic-Based Prediction Model for Multiple Traits with Binary, Ordinal, and Continuous Phenotypes.
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 50 papers.
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Who cites it
50 citing papers in PubMed.
- Research progress on genomic selection breeding technology for crops.Protoplasma · 2026Review
- Multimodality, interaction modeling, and multimodule architectures in genomic prediction: A unified conceptual framework.The plant genome · 2026Article
- Empowering Plant Biotechnology Research: Super-Pangenomes as a Novel Arsenal for Crop Breeding and Improvement.Molecular biotechnology · 2026Review
- 2NPLGBM: a genomic model that merges the strengths of classical and machine learning methods in genomic prediction.Plant methods · 2026Article
- Bayesian neural networks for genomic prediction: uncertainty quantification and SNP interpretation with SHAP and GWAS.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Integrative Machine Learning Approaches for Identifying Loci Associated with Anthracnose Resistance in Strawberry.Plants (Basel, Switzerland) · 2025Article
- Dried Blood-Rumen Content Mixtures as Sustainable Poultry Feed: A Review on Nutritional, Economic, and Environmental Potential.Food science & nutrition · 2025Review
- Advances in multi-trait genomic prediction approaches: classification, comparative analysis, and perspectives.Briefings in bioinformatics · 2025Review
- MtCro: multi-task deep learning framework improves multi-trait genomic prediction of crops.Plant methods · 2025Article
- Genomic prediction powered by multi-omics data.Frontiers in genetics · 2025Article
- Deep learning for genomic selection of aquatic animals.Marine life science & technology · 2024Review
- Application of deep learning with bivariate models for genomic prediction of sow lifetime productivity-related traits.Animal bioscience · 2024Article
- Climate change impacts on crop breeding: Targeting interacting biotic and abiotic stresses for wheat improvement.The plant genome · 2024Article
- Article
- Multimodal deep learning methods enhance genomic prediction of wheat breeding.G3 (Bethesda, Md.) · 2023Article
- Modeling genotype × environment interaction for single and multitrait genomic prediction in potato (Solanum tuberosum L.).G3 (Bethesda, Md.) · 2023Article
- The Global Assessment of Oilseed Brassica Crop Species Yield, Yield Stability and the Underlying Genetics.Plants (Basel, Switzerland) · 2022Review
- A survey of few-shot learning in smart agriculture: developments, applications, and challenges.Plant methods · 2022Review
- Bayesian multitrait kernel methods improve multienvironment genome-based prediction.G3 (Bethesda, Md.) · 2022Article
- Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction.Methods in molecular biology (Clifton, N.J.) · 2022Review
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8 authors.
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Abstract
Multiple-trait experiments with mixed phenotypes (binary, ordinal and continuous) are not rare in animal and plant breeding programs. However, there is a lack of statistical models that can exploit the correlation between traits with mixed phenotypes in order to improve prediction accuracy in the context of genomic selection (GS). For this reason, when breeders have mixed phenotypes, they usually analyze them using univariate models, and thus are not able to exploit the correlation between traits, which many times helps improve prediction accuracy. In this paper we propose applying deep learning for analyzing multiple traits with mixed phenotype data in terms of prediction accuracy. The prediction performance of multiple-trait deep learning with mixed phenotypes (MTDLMP) models was compared to the performance of univariate deep learning (UDL) models. Both models were evaluated using predictors with and without the genotype × environment (G×E) interaction term (I and WI, respectively). The metric used for evaluating prediction accuracy was Pearson's correlation for continuous traits and the percentage of cases correctly classified (PCCC) for binary and ordinal traits. We found that a modest gain in prediction accuracy was obtained only in the continuous trait under the MTDLMP model compared to the UDL model, whereas for the other traits (1 binary and 2 ordinal) we did not find any difference between the two models. In both models we observed that the prediction performance was better for WI than for I. The MTDLMP model is a good alternative for performing simultaneous predictions of mixed phenotypes (binary, ordinal and continuous) in the context of GS.
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