血蛋白质学的机器学习对间歇性肺部疾病的诊断进行了分类
Yong Huang1, Shwu-Fan Ma1, Justin M Oldham2
1Division of Pulmonary and Critical Care Medicine, University of Virginia, Charlottesville, Virginia.
American journal of respiratory and critical care medicine
|February 29, 2024
概括
使用37种蛋白质的蛋白质组分类器 (PC37) 能够有效地区分连接组织疾病相关的间歇性肺病 (CTD-ILD) 和异常性肺纤维化 (IPF). 机器学习模型准确地分类这些间歇性肺部疾病,有助于差异诊断.
科学领域:
- 肺部病理学 肺部病理学
- 蛋白质组学是指蛋白质组学.
- 生物信息学是一种生物信息学.
背景情况:
- 区分连接组织疾病相关的间歇性肺病 (CTD-ILD) 与异常性肺纤维化 (IPF) 存在重大临床挑战.
- 准确的诊断对于适当的患者管理和治疗策略至关重要.
研究的目的:
- 识别和验证能够区分CTD-ILD和IPF的蛋白质组签名.
- 开发机器学习模型,准确分类这些间歇性肺部疾病.
主要方法:
- 高通量蛋白质组学是在IPF和CTD-ILD患者的大队伍的血样本上进行的.
- 使用递归特征消除来识别37种蛋白质分类器 (PC37).
- 多个机器学习模型 (SVM,LASSO,随机森林) 在独立的数据集上进行了训练和验证,以进行分类.
主要成果:
- PC37分类器被丰富了与免疫反应和肺部发育相关的途径.
- 结合PC37,年龄和性别的机器学习模型实现了高诊断性能,AUC从0.85到0.96.
- 在单个样本数据集中,分类间歇性肺部疾病的准确性高达82.9%.
结论:
- 在蛋白质组数据上训练的机器学习模型可以可靠地区分CTD-ILD和IPF.
- 已识别的蛋白质,其中许多参与免疫通路,为疾病机制提供了洞察力.
- 开发了一种新的单样分类方法,有可能在具有挑战性的ILD病例中改善差异诊断.
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