基于机器学习算法,选最好的慢性阻塞性肺病 (COPD) 风险模型和易感性SNP
Zehua Yang1, Yamei Zheng1, Lei Zhang1
1Department of Respiratory and Critical Care Medicine, Hainan Affiliated Hospital of Hainan Medical University, Hainan General Hospital, Haikou, Hainan, 570311, People's Republic of China.
International journal of chronic obstructive pulmonary disease
|November 11, 2024
概括
遗传因素显著影响慢性阻塞性肺病 (COPD). 这项研究确定了LASSO模型是COPD的最佳预测因素,rs12479210是潜在的风险因素.
科学领域:
- 遗传学和基因组学 遗传学和基因组学
- 肺部医学 肺部医学
- 生物统计学 生物统计学
背景情况:
- 慢性阻塞性肺病 (COPD) 是一种进展性呼吸道疾病,具有显著的遗传和环境影响.
- 遗传因素在确定COPD易感性和进展方面发挥着至关重要的作用.
- 识别遗传风险因素是了解和管理COPD的关键.
研究的目的:
- 选和识别最有效的预测模型,以评估与慢性肺炎相关的单核酸多态 (SNP).
- 利用最好的预测模型来识别COPD潜在的遗传风险因素.
- 提高对COPD遗传基础的理解.
主要方法:
- 使用Agena MassARRAY平台对290名健康受试者和233名COPD患者进行基因定型.
- 使用后勤回归对146个SNP进行初始选,然后使用LASSO,RFE和随机森林算法进行特征选择.
- 使用接收器操作特征 (ROC) 曲线评估模型性能,以选择最佳预测模型.
主要成果:
- 后勤回归从最初的146个SNP中确定了44个候选SNP.
- 拉索模型以0.809的ROC曲线值展示了最高的区分性能,超过了其他测试的算法.
- 通过LASSO模型选择的25个SNP的分析表明rs12479210可能是COPD的潜在风险因素.
结论:
- 拉索回归模型被认为是COPD相关遗传变异的优越预测工具.
- 突出显示SNP rs12479210是潜在的遗传风险基因,需要进一步研究COPD病因.
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