生物信息学联合学习的技术和法律方面:应用,挑战和机会
Daniele Malpetti1,2, Marco Scutari1, Francesco Gualdi1,2
1Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), USI-SUPSI, Polo Universitario Lugano, Lugano, Switzerland.
Frontiers in digital health
|December 4, 2025
概括
联合学习通过安全地使用来自多个机构的数据,使协作生物信息学研究成为可能. 这种方法增强了基因组学和蛋白质组学方面的发现,同时保护了患者的隐私.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 联合学习 (FL) 促进跨机构的合作研究.
- 它解决了数据共享限制和患者隐私问题.
- 获得更大,多样化的数据集可以加速遗传学和系统生物学方面的发现.
研究的目的:
- 提供关于生物信息学联合学习的入门概述.
- 审查FL在蛋白质组学,GWAS,单细胞和多细胞组学研究中的关键应用.
- 讨论在生物信息学中实施FL的法律,方法和基础设施方面的挑战.
主要方法:
- 对现有文献和联合学习在生物信息学中的应用进行审查.
- 分析包括数据隐私,法律框架和技术基础设施在内的挑战.
- 探索对生物银行和数据可访问性的潜在影响.
主要成果:
- 联合学习为利用分布式数据集提供了一种保护隐私的方法.
- 关键应用范围包括蛋白质组学,全基因组关联研究 (GWAS),单细胞和多细胞组学.
- 确定的挑战包括法律,方法和基础设施障碍.
结论:
- 联合学习可以通过允许访问更广泛的数据组合来显著推进生物信息学研究.
- 广泛采用可以加速对遗传,表型和环境数据的探索和翻译.
- 克服挑战将释放FL在临床发现方面的全部潜力.
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