优化基因组预测与转移学习在脊柱回归框架下的基因组预测
Osval A Montesinos-López1, Eduardo A Barajas-Ramirez1, Josafhat Salinas-Ruiz2
1Facultad de Telemática, Universidad de Colima, Colima, México.
The plant genome
|July 18, 2025
概括
基因组选择 (GS) 的准确性通过转移学习得到改善. 转移RR和转移ARR方法在小麦和大米育种中提高了相关性预测的22%以上,NRMSE的5%以上.
科学领域:
- 农业科学 农业科学
- 遗传学 遗传学 是一个
- 生物技术是生物技术.
背景情况:
- 基因组选择 (GS) 对于在没有直接表型化的情况下预测植物和动物特征至关重要.
- 由于实验噪声,提高GS的预测准确度至关重要.
- 为了提高GS预测准确度,存在各种策略.
研究的目的:
- 探索转移学习在基因组选择中的应用.
- 评估转移学习与回归 (RR) 和分析RR (ARR) 结合,以提高预测.
- 将转移学习模型与传统的RR和ARR进行比较.
主要方法:
- 应用转移学习 (转移RR和转移ARR) 从代理到目标环境.
- 利用了11个小麦和大米的多环境数据集.
- 使用皮尔森相关性 (Cor) 和正常化根平均平方误差 (NRMSE) 评估模型性能.
主要成果:
- 转移RR和转移ARR显著改善了预测性能.
- 与非转移模型相比,转移学习方法提高了Cor的22.962%和NRMSE的5.757%.
- 经验证据支持GS转移学习的有效性.
结论:
- 转移学习,特别是转移RR和转移ARR,提供了一种强大的方法来提高基因组选择的准确性.
- 这些方法显示出改善育种计划的巨大潜力.
- 这些发现突显了利用跨环境的数据进行更准确的预测的价值.
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