基于SHAP的二元化增强了元taxonomic机器学习,并应用于炎症性肠病的肠道微生物群
Youngro Lee1,2,3,4,5, Jongmo Seo1,2,3,4,5, Barbara Di Camillo6,7,8
1Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea.
Scientific reports
|November 7, 2025
概括
我们开发了一种新的基于SHAP的微生物组数据二元化方法. 这种方法提高了机器学习模型的性能和可用于生物标志物发现的解释性,优于传统方法.
科学领域:
- 微生物组研究的研究.
- 计算生物学是一种计算生物学.
- 生物统计学 生物统计学
背景情况:
- 应用于微生物组数据的机器学习面临着由于高维度,稀疏性和相关性的挑战.
- 微生物组特征经常表现出类似二进制的特征,这表明二进制可能是可行的.
- 现有的二进制化方法可能无法最佳地利用特征贡献.
研究的目的:
- 为微生物组数据分析引入基于SHAP的新型二元化管道.
- 为了提高机器学习模型的性能和可解释性,在生物标志物发现.
- 确定强大的微生物生物标志物,用于疾病,如炎症性肠病 (IBD).
主要方法:
- 在原始连续微生物组数据上训练机器学习模型.
- 使用SHAP (夏普利增量扩展) 值来确定特征特定的二元化值.
- 使用这些值对数据集进行二元化,并重新训练模型,将性能与连续和零值数据进行比较.
- 评估了用于IBD分类的肠道微生物组数据的方法.
主要成果:
- 与连续数据和零值二元化相比,基于SHAP的二元化始终改善了分类性能和可解释性.
- 最好的模型的马修斯相关系数从0.884增加到0.928.
- 非树型模型,如物流回归和神经网络,显示出最大的性能增长.
- 确定了17个与IBD相关的微生物生物标志物的简洁集合,具有更稳定的排名.
结论:
- 基于SHAP的二元化是分析高维微生物组数据的有效策略.
- 该方法提高了预测性能,可解释性和生物标志物稳定性.
- 这种方法具有广泛的适用性,并为微生物组研究的未来扩展提供了潜力.
关键词:
选择生物标志物的选择.IBD IBD IBD IBD IBD IBD IBD IBD IBD IBD IBD IBD IBD IBD IBD微生物组是一个微生物组.这就是 SHAP SHAP 的意思.更多相关视频
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