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FAIR Data Cube, a FAIR data infrastructure for integrated multi-omics data analysis

Liao, X.; Niehues, A.; de Visser, C.; Huang, J.; Doornbos, C.; Ederveen, T. H. A.; Kulkarni, P.; van der Velde, K. J.; Swertz, M. A.; Brandt, M.; van Gool, A. J.; 't Hoen, P. A. C.

2023-04-26 health informatics
10.1101/2023.04.23.23289000 medRxiv
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MotivationWe 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 reuse 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. ResultsThe 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.

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The top 7 journals account for 50% of the predicted probability mass.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.