药物遗传学的合成数据:使可扩展和安全的研究成为可能.
Marko Miletic1, Anna Bollinger2, Samuel S Allemann2
1Institute for Optimisation and Data Analysis (IODA), Bern University of Applied Sciences, Biel, Switzerland.
JAMIA open
|October 6, 2025
概括
对于药物遗传学研究,传统的合成数据生成方法,如copula和synthpop,提供了强大的数据实用性和隐私保护平衡,在许多场景中表现优于深度学习模型.
科学领域:
- 药物遗传学 药物遗传学
- 生物信息学是一种生物信息学.
- 数据科学数据科学数据科学
背景情况:
- 合成数据生成 (SDG) 对药物遗传学 (PGx) 研究至关重要,特别是对于有限或敏感的患者数据.
- 评估各种SDG方法对于确定它们适用于复杂的PGx数据集至关重要.
- 评估数据实用性和隐私性对于负责任的数据共享和研究至关重要.
研究的目的:
- 评估七种合成数据生成 (SDG) 方法对药物遗传学 (PGx) 研究的性能.
- 将传统和基于深度学习的SDG方法与高维基基因型和表型PGx数据进行比较.
- 评估基于广泛效用,特定效用和隐私风险的SDG方法.
主要方法:
- 评估了七种SDG方法 (synthpop,avatar,copula,copulagan,ctgan,tvae,tabula) 的使用情况.
- 使用了142名患者的PGx概况,其中包括高维基基因型 (104个变量) 和表型 (24个变量) 数据的场景.
- 性能被评估使用倾向评分平均平方误差 (pMSE) 广泛的实用性,加权的F1评分特定的实用性,和隐私风险的e-识别性.
主要成果:
- 科普拉和synthpop表现一致强,平衡低隐私风险 (ε-识别:0.25-0.35) 与竞争实用性.
- 深度学习模型 (tabula,tvae) 实现了较低的pMSE,但隐私风险较高 (>0.4) 和预测收益有限.
- 特定效用 (F1得分) 与广泛效用 (pMSE) 相对关系较弱,表明分布忠实性不能保证预测相关性.
结论:
- 没有任何一种SDG方法在所有评估标准中都表现出色.
- 对于对隐私敏感的PGx研究,copula和synthpop在实用性和隐私之间提供了可靠的权衡,特别是在高维,有限样本数据集中.
- 多度指标评估是必不可少的,因为一般效用指标并不总是预测特定的预测效用.
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