Liao Xiaofeng, Ederveen Thomas H A, Niehues Anna, de Visser Casper, Huang Junda, Badmus Firdaws, Doornbos Cenna, Orlova Yuliia, Kulkarni Purva, van der Velde K Joeri, Swertz Morris A, Brandt Martin, van Gool Alain J, 't Hoen Peter A C
Medical BioSciences Department, Radboud University Medical Center, Nijmegen, The Netherlands.
SURF, Science Park 140, 1098 XG, Amsterdam, The Netherlands.
J Biomed Semantics. 2024 Dec 28;15(1):20. doi: 10.1186/s13326-024-00321-2.
We are witnessing an enormous growth in the amount of molecular profiling (-omics) data. The integration of multi-omics data is challenging. Moreover, human multi-omics data may be privacy-sensitive and can be misused to de-anonymize and (re-)identify individuals. Hence, most biomedical data is kept in secure and protected silos. Therefore, it remains a challenge to re-use these data without infringing the privacy of the individuals from which the data were derived. Federated analysis of Findable, Accessible, Interoperable, and Reusable (FAIR) data is a privacy-preserving solution to make optimal use of these multi-omics data and transform them into actionable knowledge.
The Netherlands X-omics Initiative is a National Roadmap Large-Scale Research Infrastructure aiming for efficient integration of data generated within X-omics and external datasets. To facilitate this, we developed the FAIR Data Cube (FDCube), which adopts and applies the FAIR principles and helps researchers to create FAIR data and metadata, to facilitate re-use of their data, and to make their data analysis workflows transparent, and in the meantime ensure data security and privacy.
我们正目睹分子谱分析(-组学)数据量的巨大增长。多组学数据的整合具有挑战性。此外,人类多组学数据可能对隐私敏感,可能被滥用以去匿名化和(重新)识别个人。因此,大多数生物医学数据被保存在安全且受保护的孤岛中。所以,在不侵犯数据来源个人隐私的情况下重新使用这些数据仍然是一项挑战。对可查找、可访问、可互操作和可重用(FAIR)数据进行联邦分析是一种保护隐私的解决方案,可充分利用这些多组学数据并将其转化为可操作的知识。
荷兰X-组学计划是一项国家路线图大型研究基础设施,旨在高效整合X-组学内部生成的数据和外部数据集。为便于实现这一点,我们开发了FAIR数据立方体(FDCube),它采用并应用FAIR原则,帮助研究人员创建FAIR数据和元数据,促进其数据的重新使用,并使其数据分析工作流程透明化,同时确保数据安全和隐私。