在药物发现中,用于分散和安全的机器学习的隐私保护技术
Aljoša Smajić1, Melanie Grandits1, Gerhard F Ecker1
1Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria.
Drug discovery today
|November 7, 2023
概括
分散机器学习 (ML) 技术解决了药物发现中的数据隐私挑战. 这篇概述探讨了诸如联合学习和差异隐私等方法,强调了它们在ML模型构建中的优缺点.
科学领域:
- 计算化学和化学信息学
- 人工智能在医学和药理学中的应用
背景情况:
- 数据可用性,安全性和隐私性是机器学习 (ML) 效率的关键障碍.
- 药物发现中的敏感数据需要专门的ML方法来开发模型.
研究的目的:
- 提供适用于药物发现的去中心化ML技术的概述.
- 为了说明这些新方法在制药领域的优缺点.
主要方法:
- 探索安全的多方计算.
- 对分布式深度学习框架的审查.
- 对同态加密,区块链,差异隐私和联合学习的分析.
- 检查结合多种隐私保护技术的混合方法.
主要成果:
- 分散的ML为利用药物发现中的敏感数据提供了可行的解决方案.
- 每种技术在隐私,安全和计算开销方面都有独特的优势和局限性.
- 方法的组合可能会提高整体性能和安全性.
结论:
- 分散的ML技术对于推动药物发现而保护数据至关重要.
- 仔细考虑特定技术的好处和缺点对于最佳实施至关重要.
- 对混合模型的进一步研究有望释放出对药品隐私保护ML的更大潜力.
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