project plan: How to sustainably improve biomedical (research) data management

Abstract

Several requirements, from technical (e.g. data interoperability) to juridical (e.g. data privacy), of high relevance and in some cases of contrary characteristics render digitalisation of health care in Germany at least challenging. With MeDaX (bioMedical Data eXploration) we aim at designing and implementing innovative and efficient methods for biomedical data storage, combination, enrichment, and for data retrieval and analysis based on graph technology. Following a federated approach we will provide an open source tool for building local knowledge graph instances of by default classified data. Declassification of publishable information will allow to export non-sensitive sub-graphs that can be combined into a global graph. A user interface for visualising and querying the incorporated data complete the open source and open access MeDaX information and research platform.

Keywords

• biomedical data
• data reusability
• federated knowledge graph
• information and research portal
• medax

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Sciences ∗Corresponding author. https://www.medizin.uni-greifswald.de/medizininformatik/forschung/laufende-projekte/medax/ (J. A.H. Wodke) © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings htp:/ceur-ws.org ISN1613-073

CEUR Workshop Proceedings (CEUR-WS.org) integration centres a knowledge graph tool for local application. This tool integrates health care data [ 2 ] and population studies [ 3, 4 ] with information from public databases [ 5 ] into a graph database. It semantically enriches incorporated data with ontological [ 6 ] and provenance information. And it scores the contained knowledge according to data quality, to measures for similarity analysis, and to structure-based querying.

Graph databases have been shown specifically suitable for storage of complex heterogeneous data [ 7, 8 ]. The MeDaX approach maximises benefits i) for authorised medical personnel and researchers by rendering biomedical (research) data findable, interoperable, and possibly accessible, ii) for the public by providing transparent information about biomedical data usage, and iii) ultimately for patients by considering their biomedical data both, highly interesting for exploration but also worth maximum protection.

Acknowledgments MeDaX is funded by the BMBF as part of the MIRACUM consortium within the Medical Informatics Initiative (FKZ: 01ZZ2019).

References

[1] M. D. Wilkinson , M. Dumontier , I. J. Aalbersberg , G. Appleton, M. Axton , A. Baak , N. Blomberg , J.-W. Boiten , L. B. da Silva Santos , P. E. Bourne , et al., The fair guiding principles for scientific data management and stewardship , Scientific data 3 ( 2016 ).

[2] T. Ganslandt , M. Boeker , M. Löbe , F. Prasser , J. Schepers , S. Semler , S. Thun , U. Sax, Der kerndatensatz der medizininformatik-initiative: Ein schritt zur sekundärnutzung von versorgungsdaten auf nationaler ebene, in: Forum der Medizin-Dokumentation und MedizinInformatik , volume 20 , 2018 , p. 17 .

[3] U. John , E. Hensel, J. Lüdemann , M. Piek , S. Sauer , C. Adam , G. Born , D. Alte , E. Greiser, U. Haertel , et al., Study of health in pomerania (ship): a health examination survey in an east german region: objectives and design, Sozial-und Präventivmedizin 46 ( 2001 ) 186 - 194 .

[4] H. J. Grabe , H. Assel , T. Bahls , M. Dörr , K. Endlich , N. Endlich , P. Erdmann , R. Ewert , S. B. Felix , B. Fiene , et al., Cohort profile: Greifswald approach to individualized medicine (gani_med) , Journal of translational medicine 12 ( 2014 ) 1 - 15 .

[5] S. Thun , J. Sass , A. Bartschke , M. Lehne , A. Essenwanger , E. Rinaldi , S. Rudolph , K. U. Heitmann , J. J. Vehreschild , C. von Kalle, The german corona consensus dataset (gecco): A standardized dataset for covid-19 research , BMC Med Inform Decis Mak 20 ( 2020 ).

[6] N. F. Noy , N. H. Shah , P. L. Whetzel , B. Dai , M. Dorf , N. Griffith , C. Jonquet , D. L. Rubin , M.-A. Storey , C. G. Chute , et al., Bioportal: ontologies and integrated data resources at the click of a mouse , Nucleic acids research 37 ( 2009 ) W170 - W173 .

[7] C. T. Have , L. J. Jensen , Are graph databases ready for bioinformatics? , Bioinformatics 29 ( 2013 ) 3107 .

[8] A. Fabregat , F. Korninger , G. Viteri, K. Sidiropoulos , P. Marin-Garcia , P. Ping , G. Wu, L. Stein , P. D'Eustachio , H. Hermjakob , Reactome graph database: Efficient access to complex pathway data , PLoS computational biology 14 ( 2018 ) e1005968 .