人工智能与基因组选择相遇:在各种植物数据集中比较深度学习和GBLUP
Abelardo Montesinos-López1, Osval A Montesinos-López2, Sofia Ramos-Pulido1
1Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Jalisco, Mexico.
Frontiers in genetics
|May 14, 2025
概括
深度学习 (DL) 模型在植物育种中显示出基因组选择 (GS) 的前景,在较小的数据集上经常超过基因组最佳线性无偏预测器 (GBLUP). 最佳性能需要对DL和GBLUP方法进行仔细的超参数调整.
科学领域:
- 植物遗传学 植物遗传学
- 计算生物学是一种计算生物学.
- 农业科学 农业科学
背景情况:
- 基因组选择 (GS) 对于加速植物育种至关重要.
- 基因组最佳线性无偏预测 (GBLUP) 是GS的标准方法.
- 深度学习 (DL) 提供了捕捉复杂遗传架构的潜力.
研究的目的:
- 为了比较DL模型和GBLUP在植物育种中的预测性能.
- 评估超参数调整对模型准确性的影响.
- 为选择基因组预测模型提供实用指南.
主要方法:
- 在14个现实植物育种数据集中对DL模型和GBLUP进行比较分析.
- 对每个模型和数据集进行严格的超参数调整.
- 预测准确性和可靠性的评估.
主要成果:
- 通过捕捉非线性遗传模式,DL模型经常优于GBLUP,特别是在较小的数据集中.
- 无论是DL还是GBLUP,在所有特征和场景中都始终超越了对方.
- 成功的DL模型实现高度依赖于精确的参数优化.
结论:
- 在基因组预测中,DL和GBLUP方法是互补的.
- 模型的选择应与特定的特征特征和繁殖计划的优先事项保持一致.
- 优化的基因组预测模型对于强大的植物育种结果至关重要.
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