实施PCA支持的支持矢量机,使用细胞因子来区分吸烟者与非吸烟者
Seema Singh Saharan1,2,3, Pankaj Nagar1, Kate Townsend Creasy4
1Department of Statistics, University of Rajasthan, Jaipur, India.
机器学习确定了主要的血细胞因子,使吸烟者与非吸烟者区别开来,改善了早期疾病检测,并使精确医学干预成为可能.
科学领域:
- 发现生物标志物的发现.
- 翻译医学是一种翻译医学.
- 计算生物学是一种计算生物学.
背景情况:
- 吸烟与COPD,癌症和心脏病等严重疾病有关.
- 细胞因子在与吸烟相关疾病相关的炎症反应中发挥作用.
- 早期诊断和干预对于管理吸烟相关疾病至关重要.
研究的目的:
- 调查血细胞因子水平升高与吸烟状态之间的关联.
- 开发一种机器学习模型,使用细胞因子概况来区分吸烟者和非吸烟者.
- 为了确定疾病预后和诊断的关键细胞因子生物标志物.
主要方法:
- 应用支持矢量机 (SVM) 算法用于分析65种血细胞因子和传统生物标志物.
- 使用主要组件分析 (PCA),十倍交叉验证和优化变量的重要性.
- 使用接收器操作曲线下的区域 (AUROC) 评估分类性能.
主要成果:
- 在区分吸烟者和非吸烟者方面,SVM获得了89.2%的AUROC (95% CI:85.4%,93.1%) .
- 发现的关键细胞因子包括I-TAC,G-CSF-CSF-3和MDC-CCL22.
- 使用前五种细胞因子进行优化,AUROC提高到93% (95% CI:90.1%,99.5%).
结论:
- 机器学习,特别是SVM,有效地根据血细胞因子概况识别吸烟状态.
- 选择的细胞因子作为强大的生物标志物来区分吸烟者,有助于早期发现疾病.
- 这些发现支持机器学习在吸烟相关疾病的翻译和精准医学中的应用.
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