TaxaHFE:一种机器学习方法,使用分类结构的分类结构来崩微生物组数据集
Andrew Oliver1, Matthew Kay2, Danielle G Lemay1,3,4
1USDA-ARS Western Human Nutrition Research Center, Davis, CA 95616, United States.
Bioinformatics advances
|December 4, 2023
概括
我们开发了TaxaHFE,这是一种用于减少生物数据特征的新算法. TaxaHFE利用分类层次结构来提高机器学习模型的解释性和性能,显著减少特征数量,同时提高预测准确性.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 机器学习在生物学中的应用
背景情况:
- 生物学家利用机器学习进行预测和解释.
- 减少特征可以提高模型性能和可解释性.
- 生物数据中的等级结构 (例如,微生物组分类) 在特征减少中经常未被利用.
研究的目的:
- 设计一个功能工程算法,利用生物数据中的等级关系.
- 通过使用分类信息来提高机器学习模型的可解释性和性能.
主要方法:
- 开发了TaxaHFE,这是一种将信息差特征分解为更高的分类层的算法.
- 将TaxaHFE应用于六个生物数据集.
- 使用物种级特征的机器学习模型与使用TaxaHFE预处理的特征进行了比较.
主要成果:
- 在数据集中平均减少了90%的功能.
- 使用TaxaHFE特征的模型显示,接收机操作员曲线在曲线下面的面积平均增加了3.47%.
- 对于特征崩,TaxaHFE独特地适应了分类和连续响应变量.
结论:
- TaxaHFE有效地将层次组织的特征减少到一个更具信息性的子集.
- 这种减少提高了机器学习模型在生物学研究中的可解释性.
- 该算法为分类学数据的特征工程提供了一种新的方法.
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