整合机器学习方法用于预测疾病风险,使用来自英国生物银行的多omics数据
Oscar Aguilar1, Cheng Chang2, Elsa Bismuth2
1Department of Management Science & Engineering, Stanford University, Stanford, CA, United States of America.
bioRxiv : the preprint server for biology
|April 25, 2024
概括
这项研究整合了用于疾病风险预测的多omics数据,发现它可以提高八种疾病的准确性. 代谢数据具有价值,特别是当标准生物标志物无法获得时.
科学领域:
- 生物医学信息学 生物医学信息学
- 基因组学就是基因组学.
- 代谢学 代谢学 代谢学
背景情况:
- 目前的疾病风险预测通常依赖于个人数据类型,如基因组学或人口统计学.
- 需要一个综合的多学科方法来进行全面的疾病风险评估和分层.
研究的目的:
- 开发和比较综合的多学科模型,用于疾病风险识别和生存分析.
- 评估不同类型数据的贡献,包括新陈代谢学,在多omics模型.
主要方法:
- 训练有素的预测和生存模型 (拉索回归,多层感知器,XG提升,ADA提升,Cox比例危险模型).
- 利用了多种omics数据,包括基因组,人口统计,生物标志物和代谢数据集.
- 使用ROC-AUC分数对各种疾病和特征组合进行模型性能比较.
主要成果:
- 多omics数据集成显著改善了8种疾病的风险预测.
- 代谢数据显示,与人口统计学,遗传学和生物标志物特征相比,其贡献很小.
- 当生物标志物板无法使用时,代谢学作为标准生物标志物板的可行替代品.
结论:
- 多omics数据集成是改善疾病风险识别和分层的强大工具.
- 虽然代谢组学对其他组学的影响较小,但它提供了一个有价值的替代生物标志物来源.
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