可解释机器学习利用蛋白质组学来改善心血管疾病风险预测和生物标志物识别
Héctor Climente-González1, Min Oh2, Urszula Chajewska2
1Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, The Innovation Building, Roosevelt Dr, Headington, Oxford, OX3 7FZ, United Kingdom. HECG@novonordisk.com.
Communications medicine
|May 19, 2025
概括
预测心血管疾病 (CVD) 风险至关重要. 使用英国生物银行蛋白质组学数据与机器学习,这项研究开发了一个比传统得分更准确的CVD风险预测模型.
科学领域:
- 基因组学和蛋白质组学
- 生物医学信息学 生物医学信息学
- 心血管医学 心血管医学
背景情况:
- 心血管疾病 (CVD) 是导致死亡和残疾的主要原因.
- 准确的心血管疾病风险预测对于预防和早期干预至关重要.
- 英国生物银行蛋白质组学数据为疾病关联研究提供了一个新的资源.
研究的目的:
- 使用蛋白质组学数据和临床风险因素预测10年心血管疾病风险.
- 开发一个可解释的机器学习模型用于CVD风险评估.
- 通过基因关联识别潜在的治疗点.
主要方法:
- 利用英国生物银行制药蛋白质学项目的50057名参与者 (年龄在40-69岁) 的数据.
- 雇员可解释提升机 (EBM),一个可解释的ML模型.
- 包括2923种蛋白质和55种临床风险因素作为特征;使用10倍交叉验证进行评估.
主要成果:
- 该EBM蛋白质组学模型实现了0.767的AUROC和0.241的AUPRC,超过了现有的风险得分.
- 结合临床特征提高了AUROC 0.785和AUPRC 0.284.4的性能.
- 在不同性别和种族之间展示了一致的模型性能.
结论:
- 在心血管疾病风险预测中为蛋白质组学数据分析开发了更准确和解释性的框架.
- 该方法支持个性化疾病风险预测.
- 促进了对未来药物开发的目标基因的识别.
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