机器学习和代谢学识别了与性结肠炎疾病程度相关的生物标志物
Changchang Ge1, Yi Lu1, Zhaofeng Shen1
1Department of Gastroenterology, Jiangsu Provincial Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China.
Journal of Crohn's & colitis
|February 4, 2025
概括
血清代谢分析确定了性结肠炎 (UC) 疾病程度的关键生物标志物. 这些发现有助于预测UC的进展,并为这种慢性肠道炎症疾病的治疗决策提供指导.
科学领域:
- 胃肠病学 胃肠病学
- 代谢学 代谢学 代谢学
- 生物标志物发现发现
背景情况:
- 性结肠炎 (UC) 是一种由新陈代谢影响的慢性肠道炎症疾病.
- 疾病的程度是管理UC的关键因素.
- 治疗决策和风险分层需要UC程度的非侵入性生物标志物.
研究的目的:
- 确定非侵入性血清代谢生物标志物,以确定性结肠炎疾病的程度.
- 利用机器学习进行代谢物查和预测模型构建.
主要方法:
- 坐标部分最小方程差异分析 (OPLS-DA) 用于代谢物识别.
- 机器学习算法包括LASSO,RF-RFE和SVM-RFE用于代谢物选.
- 五个机器学习模型 (XGBoost,KNN,Naive Bayes,RF,SVM) 用于预测.
主要成果:
- 在UC患者和健康对照者之间发现了220种不同的代谢物.
- 选了8种基本代谢物,以区分UC患者和HC患者.
- 与UC疾病程度相关的特定代谢物变化 (三酸,酸,阿斯巴拉基尼尔)
- 随机森林模型实现了100%的预测准确度.
结论:
- 这项研究揭示了UC的代谢概况.
- 已识别的血清代谢物作为UC疾病程度的宝贵生物标志物.
- 代谢物可以预测UC发生和进展,支持临床应用.
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