对机器学习方法的调查,应用于黄色羽毛肉的基因组预测
Bogong Liu1, Huichao Liu1, Junhao Tu1
1College of Animal Science and Technology, Hunan Agricultural University, Changsha, Hunan, China.
Poultry science
|November 21, 2024
概括
机器学习 (ML) 方法在肉养殖中表现有前途,显著提高了对半消化体重等特征的基因组预测准确度. 虽然传统方法在某些特征上表现更好,但机器学习提供了实质性的收益,特别是在优化过度参数的情况下.
科学领域:
- 动物遗传学动物遗传学
- 量化遗传学 量化遗传学
- 机器学习应用 机器学习应用
背景情况:
- 基因组育种价值对于畜牧业的改善至关重要.
- 机器学习 (ML) 在 brojiler繁殖中的应用尚未得到充分探索.
- 预测复杂的特征需要先进的分析方法.
研究的目的:
- 评估七种ML方法的有效性,以预测 brojlers的基因组繁殖值.
- 将ML性能与GBLUP和贝叶斯方法等传统方法进行比较.
- 为了确定最佳的ML算法和超参数调整用于 brojler遗传育种.
主要方法:
- 应用了七个ML算法:支持向量回归 (SVR),随机森林 (RF),梯度提升决策树 (GBDT),极端梯度提升 (XGBoost),光梯度提升机 (LightGBM),内核回归 (KRR) 和多层感知器 (MLP).
- 对产卵,生长和尸体特征的预测准确性进行了评估.
- 为了优化,研究了超参数调整和全基因组关联研究 (GWAS).
主要成果:
- 在半消化重量 (HEW) 和消化重量 (EW) 方面,ML方法显著优于GBLUP和贝叶斯方法.
- 与传统方法相比,SVR,RF,GBDT和XGBoost对HEW显示了超过60%的改善.
- 调整的超参数进一步提高了ML预测的准确性,在各种算法中取得了显著的收益.
结论:
- 机器学习方法,特别是SVR,RF,GBDT和XGBoost,为特定的肉特征提供了卓越的基因组预测准确性.
- 超参数优化对于最大限度地提高肉养殖中的ML性能至关重要.
- 将GWAS与ML集成可以进一步完善预测准确性,促进 brojler遗传改进.
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