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Smart Health pp 261-273 | Cite as

State-of-the-Art and Future Challenges in the Integration of Biobank Catalogues

  • Heimo MüllerEmail author
  • Robert Reihs
  • Kurt Zatloukal
  • Fleur Jeanquartier
  • Roxana Merino-Martinez
  • David van Enckevort
  • Morris A. Swertz
  • Andreas Holzinger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8700)

Abstract

Biobanks are essential for the realization of P4-medicine, hence indispensable for smart health. One of the grand challenges in biobank research is to close the research cycle in such a way that all the data generated by one research study can be consistently associated to the original samples, therefore data and knowledge can be reused in other studies. A catalogue must provide the information hub connecting all relevant information sources. The key knowledge embedded in a biobank catalogue is the availability and quality of proper samples to perform a research project. Depending on the study type, the samples can reflect a healthy reference population, a cross sectional representation of a certain group of people (healthy or with various diseases) or a certain disease type or stage. To overview and compare collections from different catalogues, we introduce visual analytics techniques, especially glyph based visualization techniques, which were successfully applied for knowledge discovery of single biobank catalogues. In this paper, we describe the state-of-the art in the integration of biobank catalogues addressing the challenge of combining heterogeneous data sources in a unified and meaningful way, consequently enabling the discovery and visualization of data from different sources. Finally we present open questions both in data integration and visualization of unified catalogues and propose future research in data integration with a linked data approach and the fusion of multi level glyph and network visualization.

Keywords

Biobank catalogue Linked data Minimum information about biobank data sharing (MIABIS) Knowledge discovery Visualization Glyph 

Notes

Acknowledgements

The work was performed and supported in the context of BBMRI.at the Austrian national node of BBMRI-ERIC. Our thanks are due to all partners for their contributions and various discussions and to Ms Penelope Kungl for proofreading.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Heimo Müller
    • 1
    Email author
  • Robert Reihs
    • 1
  • Kurt Zatloukal
    • 1
  • Fleur Jeanquartier
    • 2
  • Roxana Merino-Martinez
    • 3
  • David van Enckevort
    • 4
  • Morris A. Swertz
    • 4
  • Andreas Holzinger
    • 2
  1. 1.Institute of Pathology, BBMRI.atMedical University GrazGrazAustria
  2. 2.Institute for Medical Informatics, Statistics and Documentation Research Unit HCI-KDDMedical University GrazGrazAustria
  3. 3.Department of Medical Epidemiology and Biostatistics (MEB)Karolinska InstitutetStockholmSweden
  4. 4.Genomics Coordination CenterUniversity Medical Center GroningenGroningenThe Netherlands

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