整合机器学习与全转录组关联研究,以确定静脉血栓栓塞的新型预测生物标志物
Leihua Fu1,2,3, Jieni Yu1,3, Zhe Chen1,3
1Department of Hematology, Shaoxing People's Hospital, Shaoxing, Zhejiang, People's Republic of China.
British journal of haematology
|September 15, 2025
概括
这项研究引入了一种结合遗传数据和机器学习的新方法,用于预测静脉血栓栓塞 (VTE) 风险. 该方法确定了四个关键基因,提高了VTE预测准确性和生物解释性.
科学领域:
- 遗传学 遗传学 是一个
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 静脉血栓栓塞 (VTE) 是一种复杂的疾病,具有显著的遗传贡献.
- 目前使用单核酸多态 (SNP) 的风险预测工具缺乏生物解释性.
- 需要改进VTE风险预测模型,以提高准确性.
研究的目的:
- 开发一种新的,整合性的VTE风险预测方法.
- 为了确定VTE的生物学可解释的遗传预测因子.
- 通过机器学习提高VTE风险评估的准确性.
主要方法:
- 利用全转录组关联研究 (TWAS) 来识别候选基因.
- 综合患者衍生的转录基因数据用于基因改进.
- 使用机器学习算法 (LASSO,Boruta,XGBoost,随机森林,物流回归) 进行模型开发和验证.
主要成果:
- 通过TWAS识别了577个候选基因,改进为四个关键预测基因:KLKB1,ATG16L1,SELL和GLRX2.
- 在培训 (AUC 0.913-0.970) 和验证 (AUC 0.916-0.968) 队列中开发了具有高性能预测模型.
- 确认了基因对使用SHAP和回归系数进行模型预测的贡献.
结论:
- 拟议的综合性方法通过生物可解释的遗传标记增强了VTE风险预测.
- 这种方法为识别新的VTE易感基因提供了一个有希望的策略.
- 改善VTE风险预测可以促进个性化的预防和治疗策略.
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