MSXFGP:将改进的子搜索算法与XGBoost相结合,用于增强的基因组预测
Ganghui Zhou1,2, Jing Gao3,4,5, Dongshi Zuo1,2
1College of Computer and Information Engineering, Inner Mongolia Agricultural University, Erdos East Street No. 29, Hohhot, 010011, China.
BMC bioinformatics
|October 10, 2023
概括
一种新的基因组选择方法MSXFGP通过优化XGBoost参数和使用增强的子搜索算法 (SSA) 的特征选择来提高植物育种中的预测准确性. 这种方法提供了与现有方法相比的或更高的性能.
科学领域:
- 植物育种 植物育种
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 高通量测序降低成本推动了基因组选择的进步.
- 现有的基因组选择方法在实际应用中难以预测准确性.
研究的目的:
- 开发一种改进的基因组选择方法,以提高植物育种中的预测准确度.
- 为优化XGBoost参数和功能选择用于基因组预测.
主要方法:
- 拟议的MSXFGP方法使用多策略改进的子搜索算法 (SSA).
- 集成后勤混乱映射,精英学习,自适应参数调整,强制飞行和早期停止策略到SSA中.
- 优化了XGBoost参数,并使用改进的SSA.同时执行功能选择.
主要成果:
- 在6个数据集中,MSXFGP与6个现有的基因组选择模型进行了可比或更高的预测准确性.
- 该方法显示出更高的准确性,特别是当使用确定系数R2进行评估时.
- 为MSXFGP应用程序开发了一个用户友好的Python实用程序,并公开提供.
结论:
- MSXFGP为植物基因组选择提供了一种新且有效的方法.
- 该方法提供了更好的预测准确性,解决了当前基因组选择技术的局限性.
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