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谱学和机器学习方法用于系统性硬化症的临床亚型

Bartosz Miziołek^1,2, Justyna Miszczyk³, Wiesław Paja⁴

¹Department of Dermatology, Medical University of Silesia, Katowice, Poland.

Scientific reports

|February 2, 2026

概括

里埃变换红外光谱 (FTIR) 可以区分系统性硬化症 (SSc) 的亚型. 应用于FTIR光谱的机器学习模型显示了在SSc患者中进行非侵入性疾病分层和生物标志物发现的潜力.

科学领域:

生物医学光谱学生物医学光谱学
免疫皮肤学免疫皮肤学
计算生物学计算生物学

背景情况:

系统性硬化症 (SSc) 是一种复杂的自身免疫性疾病,具有多样化的临床表现.
目前SSc的诊断和分层方法可能是侵入性的和耗时的.
确定用于早期疾病检测和亚型分类的非侵入性工具至关重要.

研究的目的:

调查福里埃变换红外光谱法 (FTIR) 对全血样本的实用性,用于SSc分类.
探索多变量和机器学习技术的应用,以区分SSc亚型.
评估FTIR光谱作为SSc生物标志物发现的非侵入性工具的潜力.

主要方法:

从SSc患者的全血样本使用FTIR光谱分析.
多变量分析,包括主要成分分析 (PCA),用于分析光谱数据.
监督机器学习模型,如随机森林 (RF),被开发用于分类任务.

主要成果:

FTIR光谱检测显示了胺I/II和脂质相关区域的细微但一致的光谱差异.
PCA 显示了样品的明确聚类,表明了不同的光谱谱.
随机森林模型在分类扩散式与有限的SSc亚型方面取得了最佳性能.

结论:

与机器学习相结合的FTIR光谱显示出作为SSc疾病分层的非侵入性方法的前景.
这种方法有可能在全身性硬化症中发现生物标志物.
进一步优化模型和光谱特征提取是临床实施所必需的.

关键词:

血液中的生物标志物里埃变换红外光谱法 (FTIR) 是一种方法.机器学习机器学习主要组成部分分析 (PCA)系统性硬化症 (SSc) 是一种疾病.

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