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监督机器学习技术用于马匹的育种价值预测:使用步态视觉得分的例子.

Fernando Bussiman1, Anderson A C Alves1, Jennifer Richter1

  • 1Animal and Dairy Science Department, University of Georgia, Athens, GA 30602, USA.

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概括

机器学习模型提供了一个可行的替代方案,用于预测步态得分的马繁殖值 (EBV),显示与传统方法相比的准确性. 虽然有效,但这些人工智能方法可能会带来轻微的偏差和过度分散,特别是在年轻动物中.

关键词:
步态预测 步态预测机器学习是机器学习.支持向量的回归.视觉分数 视觉分数

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科学领域:

  • 动物遗传学动物遗传学
  • 量化遗传学 量化遗传学
  • 机器学习在动物育种中的应用

背景情况:

  • 步态得分对于马的遗传评估至关重要,但表型数据的主观性可能会阻碍遗传进步.
  • 对步态特征的客观测量对于精确的遗传选择和提高马的表现至关重要.

研究的目的:

  • 为了评估机器学习技术在预测Campolina马的五个视觉步态得分的繁殖值 (EBV) 的有效性.
  • 为了比较人工神经网络 (ANN),随机森林回归 (RFR) 和支持矢量回归 (SVR) 与传统多特征模型 (MTM) 的性能.

主要方法:

  • 利用了超过5000个表型记录的数据集和107,951匹Campolina马的14代血统.
  • 使用多特征模型 (MTM) 估计的方差组件和EBV.
  • 训练有素的ANN,RFR和SVR模型使用调整的表型和固定的效果解决方案,MTM EBV作为目标变量. 使用线性回归验证的模型.

主要成果:

  • 机器学习模型的准确性与MTM相当,而ANN的准确性略高.
  • ANN表现出最高的偏差,其次是MTM,而分散性则有所不同,ANN的偏差最高,MTM的偏差最低.
  • 所有测试的机器学习模型都被证明是步行特征EBV预测的可行替代方案.

结论:

  • 机器学习为预测主观特征的繁殖值提供了一种可行的方法,例如马的步态得分.
  • 虽然准确,但机器学习方法可能会引入轻微的偏差和过度分散,特别是在年轻的马匹中,这需要在遗传评估中仔细考虑.