PSM-SMOTE:倾向性得分匹配和合成少数人过量采样,用于处理不平衡的微生物群数据
Jeongsup Moon1, Zhe Liu1, Taesung Park2,3
1Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
Genes & genomics
|October 4, 2025
概括
一种新方法PSM-SMOTE通过结合倾向得分匹配 (PSM) 和边界合成少数群体过量采样技术 (borderline-SMOTE) 来有效处理不平衡的微生物组数据,从而提高预测模型的性能.
科学领域:
- 微生物组数据分析分析
- 生物信息学是一种生物信息学.
- 机器学习在医疗保健中的应用
背景情况:
- 微生物组预测模型面临着共同变量和阶级不平衡的挑战,导致结果偏差.
- 倾向性得分匹配 (PSM) 解决了共变异不平衡,但减少了样本大小.
- 边界线合成少数群体过量抽样技术 (borderline-SMOTE) 过量抽样少数群体,但可能会创建信息不足的数据点.
研究的目的:
- 引入和评估PSM-SMOTE,一种新的混合采样技术.
- 为了解决微生物组数据集中的共同变量和类不平衡.
- 用微生物组数据提高预测模型的性能.
主要方法:
- 开发了一个三步混合算法:PSM,差异标记选择和边界-SMOTE应用.
- 使用了PSM,用于共变量平衡,具有四个口径级别.
- 使用七个统计测试与错误发现率校正选择了强大的差异标记.
- 在基于标记器的距离矩阵上应用边界-SMOTE,用于少数群体类型的过量抽样.
- 在胰腺管道腺癌,结直肠癌和肥胖微生物组数据集上使用后勤回归,随机森林和支持矢量机器分类器进行评估.
- 使用ROC曲线下的面积 (AUC) 评估性能.
主要成果:
- 与单独使用PSM相比,PSM-SMOTE在各种模型数据集组合中证明了测试AUC的改善.
- 随机森林模型显示,在PDAC和肥胖群体中,PSM-SMOTE的AUC增强一致.
- 支持矢量机器模型在结直肠癌队列中实现了显著的AUC增加.
- 在严格匹配条件下,物流回归模型表现出适度的AUC改进.
结论:
- PSM-SMOTE有效地解决了微生物组数据中的双重失衡问题.
- 该方法始终改善预测模型的性能.
- 对于分析不平衡的微生物组数据,PSM-SMOTE提供了一个实用且有效的解决方案.
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