使用公共数据对未引起的与引起的静脉血栓栓塞的转录和基于机器学习的分类
Yajing Li1,2, Hongru Deng2, Yongquan Gu1
1Department of Vascular Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
IET systems biology
|February 28, 2026
概括
区分引起的和未引起的静脉血栓塞栓症 (VTE) 是非常重要的. 转录基因分析和人工智能准确地分类了VTE亚型,为改进诊断和风险评估铺平了道路.
科学领域:
- 生物化学 生物化学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 将静脉血栓塞栓症 (VTE) 分类为被诱导和未被诱导的亚型对于指导抗凝治疗和评估复发风险至关重要.
- 目前用于VTE分类的临床表型方法存在局限性.
- 准确区分VTE亚型对于个性化患者管理至关重要.
研究的目的:
- 使用全血转录基因数据,识别引起和未引起的VTE之间的分子差异.
- 开发一种机器学习模型,根据基因表达模式对VTE亚型进行分类.
- 评估结合人工智能的转录形状分析的诊断潜力,用于VTE分类.
主要方法:
- 来自GSE48000数据集的全血转录组的分析,以确定引起和未引起的VTE之间的差异表达基因 (DEGs).
- 对DEGs进行了基因本体学 (GO) 和KEGG通路丰富分析.
- 随机森林算法被用于特征排名,随后开发了一个使用前30个基因的人工神经网络 (ANN) 模型.
- 模型性能使用分层的10倍交叉验证和接收器操作特征 (ROC) 分析进行了评估.
主要成果:
- 鉴定出了30个基因的特征,有效地区分了被诱导和未被诱导的VTE亚型.
- 大多数已识别的基因在未被诱导的VTE中表达较低,例如GDF2,LGALS2和LOC100130229的特定基因表现出显著的上调.
- 富化分析表明,免疫调节和囊泡运输通路在VTE病变发生过程中的参与.
- 开发的ANN模型实现了0.799的曲线下的面积 (AUC),证明了优异的歧视.
结论:
- 与机器学习相结合的转录形状分析可以准确地区分被诱导和未被诱导的VTE.
- 人工智能驱动的分子诊断对VTE分类和风险分层有希望.
- 需要在更大,独立的队列中进行前性验证,以确认这些发现并支持临床实施.
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