在植物育种中扩大基因组预测:利用大数据,机器学习和先进的软件
José Crossa1, Johannes W R Martini2, Paolo Vitale3
1International Maize and Wheat Improvement Center (CIMMYT), Carretera México - Veracruz Km. 45, El Batán, CP 56237, Texcoco, Edo. de México, Mexico; Colegio de Postgraduados, Montecillos, Edo. de México CP 56230, Mexico.
Trends in plant science
|January 31, 2025
概括
植物育种中的基因组选择 (GS) 通过机器学习 (ML) 方法变得越来越有效. 新的统计ML工具和软件正在使基因组预测 (GP) 对各种特征更容易获得和更有效.
科学领域:
- 农业科学 农业科学
- 遗传学 遗传学是一种遗传学.
- 计算机科学 计算机科学
背景情况:
- 基因组选择 (GS) 对于提高植物育种中的遗传收益越来越重要.
- GS的效率依赖于复杂的统计和计算方法.
研究的目的:
- 审查统计机器学习 (ML) 方法和软件,以提高基因组选择 (GS) 的效率.
- 探索ML如何在植物育种中民主化基因组预测 (GP) 方法.
主要方法:
- 对基因组启用预测的统计ML工具的审查.
- 对各种表型预测 (连续,二进制,分类,计数) 的ML方法的检查.
- 在基因组预测 (GP) 中强调深度学习 (DL) 模型.
主要成果:
- 统计ML工具显著提高了GS的效率,特别是在大数据方面.
- 有各种ML工具可用于预测不同类型的植物特征.
- 深度学习模型为基因组预测提供了独特的优势.
结论:
- 机器学习和可访问的软件是民主化植物育种中基因组选择的关键.
- 机器学习的进步提高了对各种植物特征的预测,加速了遗传收益.
- 数据管理工具对于采用先进的GS方法至关重要.
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