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可解释的人工智能用于植物育种中的基因型-表型预测:用来自杏仁生殖质收集数据集的案例研究

Pierfrancesco Novielli^1,2, Donato Romano^1,2, Stefano Pavan¹

¹Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy.

Frontiers in plant science

|September 25, 2024

概括

可解释的人工智能通过从基因组数据准确预测表型来改善植物育种. 这种方法识别了像杏仁外分数这样的特征的关键遗传因素,有助于可持续农业.

科学领域:

植物基因组学植物基因组学
农业科学农业科学
生物信息学是一种生物信息学.

背景情况:

DNA测序的进步已经改变了植物基因组学和遗传多样性研究.
从基因组数据预测植物表型,这对于育种至关重要,仍然具有挑战性.
确定准确的表型预测的关键遗传因素是一个重大障碍.

研究的目的:

整合可解释的人工智能 (XAI) 与机器学习进行基因型到表型预测.
提高植物育种中预测模型的准确性和可解释性.
通过阐明对特征的遗传影响来支持明智的育种决策.

主要方法:

多种机器学习 (ML) 方法的基因型到表型预测的比较.
应用回归模型来预测使用生殖质数据的杏仁外分数.
使用SHAP (夏普利增量解释) 值进行模型解释性和特征重要性分析.

主要成果:

随机森林模型实现了杏仁削分数的最佳预测性能 (R2 = 0.511 ± 0.025).
SHAP分析确定了与炮击百分比相关的显著基因组区域.
确定了一个非常重要的基因组区域,可能与参与种子发育的基因有关.

结论:

XAI算法提高了基因型到表型模型的解释性.
影响炮击百分比的遗传多态性被使用XAI.确定.
这些发现证明了XAI在优化可持续农业的作物生产方面的价值.

关键词:

杏仁是一种杏仁.可解释的人工智能基因型-表型预测预测机器学习是机器学习.贝的分数部分.

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