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使用机器学习技术预测哈纳利羊的遗传优点

Spandan Shashwat Dash¹, Yogesh C Bangar², Ankit Magotra³

¹Department of Animal Genetics and Breeding, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar, Haryana, 125004, India.

Tropical animal health and production

|June 3, 2025

概括

渐变增强机 (GBM) 准确预测了哈纳利羊的遗传优点. 这种机器学习方法通过优化选择和加速遗传进步来增强动物育种计划.

科学领域:

动物科学动物科学
遗传学遗传学是一个
机器学习机器学习

背景情况:

基因组和表型数据对于改善动物育种至关重要.
精确预测遗传优点对于高效的牲畜改进至关重要.

研究的目的:

评估七种用于预测哈纳利羊的遗传优点的机器学习算法.
确定最有效的算法,以优化该品种的育种计划.

主要方法:

测试了七个机器学习算法 (KNN,MLR,BR,SVM,ANN,RF,GBM).
使用了来自2036只哈纳利羊羔 (1998-2021) 的血统和表型数据.
模型被训练在75%的数据上,并对25%的数据进行测试,使用R2,RMSE,MAE,AIC,BIC和偏差进行评估.

主要成果:

梯度增强机 (GBM) 是表现最好的模型.
GBM 实现了0.64 的确定系数 (R2) 和 0.80.8 的预测精度 (r).
与其他算法相比,GBM在较低的错误和偏差指标下表现出卓越的性能.

结论:

机器学习,特别是GBM,显示了加强哈纳利绵羊繁殖计划的巨大潜力.
使用GBM准确预测繁殖值,可以加速绵羊种群的遗传进步.
这项研究强调了先进的计算方法在现代动物遗传学和育种中的实用性.

关键词:

繁殖价值繁殖价值繁殖价值哈尔纳利羊是哈尔纳利人的羊.机器学习机器学习预测预测预测

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