将机器学习与SHAP集成,以发现骨关节炎进展中的多组织分子特征
Jifeng Zhao1,2,3, Jiasheng Tao1,2,3, Yizhe Song1,2,3
1The Fifth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
PloS one
|March 9, 2026
概括
这项研究使用机器学习识别了骨关节炎 (OA) 的新型组织特异性生物标志物. 这些发现提供了新的诊断工具和对OA分子机制的见解.
科学领域:
- 生物标志物发现发现
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 骨关节炎 (OA) 是一种复杂的关节疾病,分子机制不明.
- 目前对骨关节炎的诊断方法缺乏特定于组织的分子标记物.
- 了解OA的多组织参与对于开发有效诊断至关重要.
研究的目的:
- 使用整合性机器学习 (ML) 框架识别骨关节炎 (OA) 的特定组织诊断生物标志物.
- 探索骨,突和血液中OA背后的分子机制.
- 开发和验证高性能OA诊断的ML模型.
主要方法:
- 通过GEO数据库利用了来自软骨,突和血液的基因表达数据.
- 应用多个ML算法 (LASSO,SVM-RFE,RF) 进行特征选择和诊断模型构建.
- 综合的夏普利添加剂解释 (SHAP) 用于模型解释性和生物标志物验证.
主要成果:
- 分别在软骨,突和血液中确定了8,28和61个差异表达基因 (DEGs).
- 发现了特定组织的生物标志物,包括CSN1S1,ABCA6 (软骨),SCRG1,CXCL2 (突),以及GNL3L,C6orf111 (血液).
- 实现了高诊断性能,AUC为0.839 (软骨),0.934 (阴膜) 和0.892 (血液).
结论:
- ML框架成功地在多种组织中确定了可靠的OA生物标志物.
- SHAP分析证实了关键生物标志物的预测重要性,如CSN1S1,ABCA6,SCRG1,CXCL2,GNL3L和C6orf111.
- 这项研究为OA生物标志物发现提供了强大的方法,并支持开发新型诊断工具.
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