ArticleFrontiers in genetics2020
Applications of Support Vector Machine in Genomic Prediction in Pig and Maize Populations.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed.
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- 2NPLGBM: a genomic model that merges the strengths of classical and machine learning methods in genomic prediction.Plant methods · 2026Article
- Predicting maize hybrid performance with machine learning and a locus-specific weighted degree of dominance transformation.Frontiers in plant science · 2026Article
- Life at the extremes: maximally divergent microbes with similar genomic signatures linked to extreme environments.NAR genomics and bioinformatics · 2025Article
- Machine learning for genomic prediction of growth traits in aquaculture: a case study of the Australasian snapper (Chrysophrys auratus).BMC bioinformatics · 2025Article
- Enhancing genomic predictive ability of yield and yield-related traits in spring wheat by integrating major plant adaptation genes as a fixed effect.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Genomic Selection for Cashmere Traits in Inner Mongolian Cashmere Goats Using Random Forest, Gradient Boosting Decision Tree, Extreme Gradient Boosting and Light Gradient Boosting Machine Methods.Animals : an open access journal from MDPI · 2025Article
- Gaining insights into epigenetic memories through artificial intelligence and omics science in plants.Journal of integrative plant biology · 2025Review
- Mutual information stacking method for prediction of the growth traits in pigs.Briefings in bioinformatics · 2025Article
- Genomic prediction with kinship-based multiple kernel learning produces hypothesis on the underlying inheritance mechanisms of phenotypic traits.Genome biology · 2025Article
- Genomic selection in pig breeding: comparative analysis of machine learning algorithms.Genetics, selection, evolution : GSE · 2025Article
- Exploiting historical agronomic data to develop genomic prediction strategies for early clonal selection in the Louisiana sugarcane variety development program.The plant genome · 2025Article
- Article
- KPRR: a novel machine learning approach for effectively capturing nonadditive effects in genomic prediction.Briefings in bioinformatics · 2024Article
- A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding.Genetics · 2024Article
- Machine Learning for the Genomic Prediction of Growth Traits in a Composite Beef Cattle Population.Animals : an open access journal from MDPI · 2024Article
- Incremental Inverse Design of Desired Soybean Phenotypes.ACS omega · 2024Article
- Supervised Machine Learning Techniques for Breeding Value Prediction in Horses: An Example Using Gait Visual Scores.Animals : an open access journal from MDPI · 2024Article
- Residual networks without pooling layers improve the accuracy of genomic predictions.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Review of applications of artificial intelligence (AI) methods in crop research.Journal of applied genetics · 2024Review
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8 authors.
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Abstract
Genomic prediction (GP) has revolutionized animal and plant breeding. However, better statistical models that can improve the accuracy of GP are required. For this reason, in this study, we explored the genomic-based prediction performance of a popular machine learning method, the Support Vector Machine (SVM) model. We selected the most suitable kernel function and hyperparameters for the SVM model in eight published genomic data sets on pigs and maize. Next, we compared the SVM model with RBF and the linear kernel functions to the two most commonly used genome-enabled prediction models (GBLUP and BayesR) in terms of prediction accuracy, time, and the memory used. The results showed that the SVM model had the best prediction performance in two of the eight data sets, but in general, the predictions of both models were similar. In terms of time, the SVM model was better than BayesR but worse than GBLUP. In terms of memory, the SVM model was better than GBLUP and worse than BayesR in pig data but the same with BayesR in maize data. According to the results, SVM is a competitive method in animal and plant breeding, and there is no universal prediction model.
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