将人工和卷积神经网络与小麦基因组预测的传统模型进行比较
1Department of Applied Mathematics, College of Science, China Agricultural University, Beijing, 100083 China.
Molecular breeding : new strategies in plant improvement
|September 15, 2025
概括
传统的贝叶斯模型在基因组预测中显示出更高的准确性,特别是在考虑基因型与环境相互作用时. 机器学习模型,如人工神经网络,根据预测场景提供互补的好处.
科学领域:
- 农业科学 农业科学
- 遗传学 是一个遗传学.
- 计算生物学 计算生物学
背景情况:
- 基因组预测对于畜牧和作物育种至关重要,模型选择会影响准确性.
- 机器学习模型正在成为基因组预测的潜在进步.
研究的目的:
- 将机器学习模型 (ANN,CNN) 与传统模型 (GBLUP,BRR,BayesA,BayesB) 的预测精度进行比较.
- 在不同的场景下评估模型性能,包括基因型与环境相互作用和交叉验证策略.
主要方法:
- 将人工神经网络 (ANN) 和卷积神经网络 (CNN) 与GBLUP,BRR,BayesA和BayesB.B.进行比较.
- 使用了三个小麦谷物产量数据集.
- 评估的模型具有和没有基因型对环境 (GxE) 相互作用.
- 采用了两个交叉验证策略 (CV1,CV2).
主要成果:
- 当包括GxE相互作用时,传统的贝叶斯模型 (BayesA,BayesB,BRR) 的表现优于其他模型.
- 只有在CV1和当忽略GxE相互作用时,ANN和CNN的准确性更高.
- 机器学习模型的性能与交叉验证策略和GxE治疗有显著差异.
结论:
- 机器学习模型可以补充传统的基因组预测方法.
- 模型优越性取决于场景,特别是在GxE交互和交叉验证方面.
- 人工神经网络在这个基因组预测背景下通常表现优于卷积神经网络.
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