剩余网络提高了基因组选择的预测准确性
Huaxuan Wu1, Bingxi Gao1, Rong Zhang1
1College of Animal Science and Technology, Yangtze University, Jingzhou, Hubei, China.
Animal genetics
|May 15, 2024
概括
剩余网络 (ResNet) 显示出改善动物和植物育种中的基因组选择 (GS) 准确性的前景. 这种深度学习方法克服了退化问题,提高了复杂特征的预测.
科学领域:
- 动物和植物育种 动物和植物育种
- 遗传学 是一个遗传学.
- 机器学习 机器学习
背景情况:
- 精确估计育种价值对于复杂特征的遗传改进至关重要.
- 由于降解问题,传统的基因组选择 (GS) 方法面临深度学习模型的挑战.
- 剩余网络 (ResNet) 旨在减轻深度学习中的梯度退化.
研究的目的:
- 评估ResNet在基因组选择 (GS) 中的性能,与传统和其他深度学习方法相比.
- 确定影响ResNet在GS中的预测准确度的因素.
- 探索转移学习与GS的ResNet的潜力.
主要方法:
- 使用皮尔森相关系数 (PCC) 和平均平方误差 (MSE) 的预测准确度的比较.
- 评估传统的基因组学最好的线性无偏预测,贝叶斯方法 (贝叶斯A,贝叶斯B,贝叶斯C,贝叶斯拉索),卷积神经网络和ResNet.
- 在小麦,模拟和真实猪数据集上应用ResNet,包括数据聚类和转移学习.
主要成果:
- 在小麦和模拟数据上的PCC和MSE中,ResNet的表现优于其他方法.
- 对于猪背脂深度,ResNet获得了最低的MSE,而贝叶斯拉索的PCC最高.
- 在特定的猪数据集群中,ResNet表现出卓越的预测准确性,而转移学习提高了其性能.
结论:
- ResNet显示了提高复杂特征的GS预测准确性的潜力.
- 遗传架构,数据量和异质性等因素可以影响ResNet的有效性.
- ResNet提供了一种有希望的深度学习方法,用于推进基因组选择.
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