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多输出和堆叠方法对基因型的料效率预测的影响,使用机器学习算法.

Mónica Mora1,2, Pablo González3, José Ramón Quevedo3

  • 1Departamento de Ciencia Animal, Universidad Politècnica de València, Valencia, Spain.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie
|July 5, 2023
PubMed
概括

提高牲畜的料效率至关重要. 这项研究发现,使用成分特征预测剩余料摄入量 (RFI) 与从猪基因型直接预测相比,并没有提高准确性.

关键词:
国家统一计划 (SNP) 是一个国家统一计划.人工智能的人工智能是人工智能.多个特征的多个特征.回归问题回归问题剩余料摄入量的剩余料摄入量堆叠堆叠 在堆叠堆叠.

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科学领域:

  • 动物科学动物科学
  • 遗传学 遗传学 是一个
  • 农业经济学 农业经济学

背景情况:

  • 料效率是肉类生产的主要经济驱动因素.
  • 剩余料摄入量 (RFI) 是选择的一个关键特征,计算为实际和预期料摄入量之间的差异.
  • 使用单输出机器学习模型对RFI的基因组预测已经显示出有限的成功.

研究的目的:

  • 评估新的策略,以改善生长猪中RFI的基因组预测.
  • 将间接预测 (多输出,单输出) 和直接预测 (堆叠) 方法与基准单输出策略进行比较.
  • 评估使用单核酸多形态 (SNP) 进行RFI预测的影响.

主要方法:

  • 实施了四种策略:单输出,多输出和堆叠,使用随机森林 (RF) 和支持向量回归 (SVR) 算法.
  • 利用了来自5828只生长猪和45610只SNP的基因组数据.
  • 采用嵌套交叉验证方案来评估不同数量的信息SNP的预测性能.

主要成果:

  • 基准单个输出策略,仅使用基因型,始终优于间接预测方法 (多个输出,堆叠).
  • 预测性能达到1000个信息SNP的峰值,但特征选择稳定性很低.
  • 获得的最佳预测性能指标是使用RF与1000个SNP的斯皮尔曼相关性 (0.23),零-一损失 (0.83) 和排名距离损失 (0.33).

结论:

  • 结合预测的组件特征 (DFI,ADG,MW,BFT) 不会提高RFI预测的准确性.
  • 使用SNP的直接基因组预测仍然是改善生长猪RFI的最有效策略.
  • 进一步的研究可能会探索替代的基因组预测模型或特征选择技术.