基因组预测的惩罚性回归方法减少了训练和测试集之间的不匹配
Osval A Montesinos-López1, Cristian Daniel Pulido-Carrillo1, Abelardo Montesinos-López2
1Facultad de Telemática, Universidad de Colima, Colima 28040, Mexico.
Genes
|August 29, 2024
概括
通过对训练数据进行加权,基因组选择的准确性得到了提高. 这种方法减轻了培训和测试组之间存在差异的特征,提高了植物育种中的预测模型性能.
科学领域:
- 农业科学 农业科学
- 遗传学 遗传学 是一个
- 生物信息学是一种生物信息学.
背景情况:
- 基因组选择 (GS) 通过降低表型化成本,彻底改变了植物育种.
- GS准确性受到训练测试集不匹配的影响,限制了预测模型的有效性.
- 现有的方法很难调整模型以针对人口的遗传和环境变异.
研究的目的:
- 为基因组选择模型开发一种新的权重策略.
- 提高植物育种中预测模型的准确性和效率.
- 为了减轻培训测试对基因组预测的差异的影响.
主要方法:
- 引入了二进制-拉索回归方法来估计特征重要性 (β系数).
- 在拉索,里奇和弹性网模型 (WLasso,WRidge,WElastic Net) 中应用的反向β系数作为权重.
- 权重模型优先考虑在培训和测试集之间不那么歧视的特征,使用glmnet库.
主要成果:
- 在六个不同的数据集中,预测准确度的持续改进.
- 证明了正常化根平均平方误差 (NRMSE) 的显著降低.
- 验证了拟议的权重策略在提高基因组预测方面的有效性.
结论:
- 开发的权重方法有效地提高了基因组选择的准确性.
- 这种方法为培训-测试组在植物育种中的不匹配提供了一个实际的解决方案.
- 该方法的简单实施使其在基因组预测中得到更广泛的采用.
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