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保护隐私的联邦无监督域适应从DNA甲基化数据的年龄预测
Cem Ata Baykara1, Ali Burak Ünal1,2, Nico Pfeifer2
1Medical Data Privacy and Privacy-Preserving Machine Learning, University of Tübingen, Tübingen, Germany.
Bioinformatics (Oxford, England)
|August 22, 2025
概括
我们开发了一种保护隐私的联合方法, 这种方法促进了生物医学研究的安全跨机构合作.
科学领域:
- 生物医学数据分析
- 医疗保健中的机器学习
- 计算生物学
背景情况:
- 由于领域的转移和隐私限制,将机器学习模型推广到小型,高维的生物数据集是具有挑战性的.
- 现有的联邦域调整 (FDA) 方法,通常基于深度学习,不适合生物医学中对隐私敏感的小规模回归任务.
研究的目的:
- 引入一个保护隐私的联合方法,用于回归无监督域调整.
- 通过分布式,高维度的生物数据集实现强大的学习,同时保持完整的数据隐私.
主要方法:
- 开发了一种使用高斯过程进行域适应的新型联合方法.
- 实施隐私保护技术,包括随机编码和安全聚合.
- 专门为小规模的高维度生物数据设计了该方法.
主要成果:
- 该方法使得高斯过程的分布式训练能够在完全数据隐私的情况下进行域调整.
- 通过DNA甲基化数据实现了与非私人最先进的年龄预测方法相比较的性能.
- 对于小规模,高维度生物数据的可扩展性和概括性.
结论:
- 这项工作使得在生物医学研究中实现安全有效的跨机构合作,而无需共享原始数据.
- 拟议的方法克服了现有的FDA对隐私敏感回归问题的限制.
- 在异质生物数据集中提供强大的机器学习模型概括.
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