Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, People's Republic of China.
Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, 430070, Hubei, People's Republic of China.
Genome Biol. 2020 Jun 17;21(1):146. doi: 10.1186/s13059-020-02052-w.
Advances in high-throughput sequencing technologies have reduced the cost of genotyping dramatically and led to genomic prediction being widely used in animal and plant breeding, and increasingly in human genetics. Inspired by the efficient computing of linear mixed model and the accurate prediction of Bayesian methods, we propose a machine learning-based method incorporating cross-validation, multiple regression, grid search, and bisection algorithms named KAML that aims to combine the advantages of prediction accuracy with computing efficiency. KAML exhibits higher prediction accuracy than existing methods, and it is available at https://github.com/YinLiLin/KAML.
高通量测序技术的进步极大地降低了基因分型的成本，并导致基因组预测在动植物育种中得到广泛应用，并且在人类遗传学中也越来越普及。受线性混合模型高效计算和贝叶斯方法精确预测的启发，我们提出了一种基于机器学习的方法，该方法结合了交叉验证、多元回归、网格搜索和二分算法，名为 KAML，旨在结合预测准确性和计算效率的优势。KAML 表现出比现有方法更高的预测准确性，并且可以在 https://github.com/YinLiLin/KAML 上获得。
