Applications of Support Vector Machine in Genomic Prediction in Pig and Maize Populations - Zhao, Wei; Lai, Xueshuang; Liu, Dengying; Zhang, Zhenyang; Ma, Peipei; Wang, Qishan; Zhang, Zhe; Pan, Yuchun (Frontiers in Genetics, 2020)
Genomic prediction (GP) has revolutionized animal and plant breeding. However, there is still a requirement for better statistical models that can improve the accuracy of GP. For this reason, in this study we explored the genomic based prediction performance of one popular machine learning methods: the support vector machine (SVM) model. We selected the most suitable kernel function and hyperparameters for SVM model in 8 published genomic data sets of pig and maize. Next, we compared SVM model with RBF and linear kernel functions to two most commonly used genome-enabled prediction models (GBLUP and BayesR) in terms of prediction accuracy, time consuming and memory usage. Results show that the best prediction performance in two of the eight data sets were found under the SVM model, but in general the predictions of both models were similar. In terms of time, SVM model was better than BayesR but worse than GBLUP. In terms of memory, SVM model was better than GBLUP and worse than BayesR in pig data but 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.