在微生物组疾病分类管道中探索规范化和特征选择的作用
Ignacio Garach Vélez1, Francisco Manuel Ortuño Guzmán1, Ignacio Rojas Ruiz1
1Department of Computer Engineering, Automation and Robotics (ICAR), University of Granada, 18071 Granada, Spain.
GigaScience
|September 2, 2025
概括
通过减少数据复杂性,特征选择管道增强了微生物群疾病分类. 最少冗余最大相关性 (mRMR) 和最小绝对缩小和选择运营商 (LASSO) 已被证明是确定强大的生物标志物的最有效方法.
科学领域:
- 微生物组分析
- 生物信息学
- 机器学习
背景情况:
- 16S rRNA微生物组数据提出了诸如高维度,组成性和稀疏性等挑战.
- 机器学习模型的有效性往往受到小样本的限制.
- 对微生物组数据的特征选择和规范化的比较研究很少.
研究的目的:
- 评估各种特征选择技术和规范化策略对基于微生物群的疾病分类的影响.
- 确定最佳的生物标志物发现方法组合和提高分类性能.
- 为了比较不同特征选择算法的有效性.
主要方法:
- 评估多种特征选择技术 (例如mRMR,LASSO,自动编码器,相互信息,ReliefF).
- 评估不同的规范化策略 (例如,中心日志比率,存在缺席).
- 整合特征选择与机器学习分类器 (例如,物流回归,SVM,随机森林).
主要成果:
- 中心日志比率正常化增强了后勤回归和SVM性能.
- 随机森林模型在相对丰富的情况下表现良好.
- 最低冗余最大相关性 (mRMR) 和LASSO提供了紧的功能集和与其他方法相比的性能,LASSO提供了更短的计算时间.
- 存在缺席正常化实现了与基于丰度的方法相似的性能.
结论:
- 通过减少特征空间,特征选择管道显著提高了模型的焦点和稳定性.
- 在不同数据集中,mRMR和LASSO被认为是最有效的特征选择方法.
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