使用FedProt进行保护隐私的多中心差异蛋白质丰度分析
Yuliya Burankova1,2, Miriam Abele3,4, Mohammad Bakhtiari5
1Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany. yuliya.burankova@tum.de.
Nature computational science
|July 11, 2025
概括
通过FedProt,可以在各个机构进行保护隐私的蛋白质组分析. 这种联合学习工具确保了准确的差异蛋白质量化,而不会影响患者数据的隐私.
科学领域:
- 蛋白质组学是指蛋白质组学.
- 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:
- 定量质谱法允许大规模的蛋白质量化.
- 对分布式患者数据的协作分析增强了统计能力,但存在隐私风险.
研究的目的:
- 介绍FedProt,这是一个新的隐私保护工具,用于协作差异蛋白质丰度分析.
- 使用联合学习实现安全,多机构的蛋白质组数据分析.
主要方法:
- 开发了FedProt,利用联合学习和添加式秘密共享.
- 创建了两个新的多中心数据集 (大肠杆菌和人血清) 用于评估.
- 我们将FedProt的准确性与现有方法 (如DEqMS) 进行了比较.
主要成果:
- FedProt的准确度与集中分析 (DEqMS) 相同,差异微不足道 (≤4×10−12).
- 与集中分析相比,元分析方法在结果中显示出显著的差异.
- 在微生物和人血清数据集上证明了FedProt的有效性.
结论:
- FedProt为协作蛋白质组分析提供了一个安全而准确的解决方案.
- 该工具有效地解决了多机构数据共享中的隐私问题.
- 在保护隐私的计算生物学中,FedProt代表了重大进步.
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