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[Strategies for biobank networks. Classification of different approaches for locating samples and an outlook on the future within the BBMRI-ERIC]. / Strategien zur Vernetzung von Biobanken. Klassifizierung verschiedener Ansätze zur Probensuche und Ausblick auf die Zukunft in der BBMRI-ERIC.
Lablans, Martin; Kadioglu, Dennis; Mate, Sebastian; Leb, Ines; Prokosch, Hans-Ulrich; Ückert, Frank.
Afiliação
  • Lablans M; Medizinische Informatik in der Translationalen Onkologie, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, 69120, Heidelberg, Deutschland. m.lablans@dkfz.de.
  • Kadioglu D; Institut für medizinische Biometrie, Epidemiologie und Informatik (IMBEI), Universitätsmedizin Mainz, 55101, Mainz, Deutschland.
  • Mate S; Lehrstuhl für Medizinische Informatik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Deutschland.
  • Leb I; Lehrstuhl für Medizinische Informatik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Deutschland.
  • Prokosch HU; Lehrstuhl für Medizinische Informatik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Deutschland.
  • Ückert F; Medizinische Informatik in der Translationalen Onkologie, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, 69120, Heidelberg, Deutschland.
Article em De | MEDLINE | ID: mdl-26753865
ABSTRACT

BACKGROUND:

Medical research projects often require more biological material than can be supplied by a single biobank. For this reason, a multitude of strategies support locating potential research partners with matching material without requiring centralization of sample storage.

OBJECTIVES:

Classification of different strategies for biobank networks, in particular for locating suitable samples. Description of an IT infrastructure combining these strategies. MATERIALS AND

METHODS:

Existing strategies can be classified according to three criteria (a) granularity of sample data coarse bank-level data (catalogue) vs. fine-granular sample-level data, (b) location of sample data central (central search service) vs. decentral storage (federated search services), and (c) level of automation automatic (query-based, federated search service) vs. semi-automatic (inquiry-based, decentral search). All mentioned search services require data integration. Metadata help to overcome semantic heterogeneity.

RESULTS:

The "Common Service IT" in BBMRI-ERIC (Biobanking and BioMolecular Resources Research Infrastructure) unites a catalogue, the decentral search and metadata in an integrated platform. As a result, researchers receive versatile tools to search suitable biomaterial, while biobanks retain a high degree of data sovereignty.

CONCLUSIONS:

Despite their differences, the presented strategies for biobank networks do not rule each other out but can complement and even benefit from each other.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Sistemas de Gerenciamento de Base de Dados / Sistema de Registros / Bases de Dados Factuais / Bancos de Espécimes Biológicos / Pesquisa Biomédica / Relações Interinstitucionais Tipo de estudo: Prognostic_studies País como assunto: Europa Idioma: De Ano de publicação: 2016 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Sistemas de Gerenciamento de Base de Dados / Sistema de Registros / Bases de Dados Factuais / Bancos de Espécimes Biológicos / Pesquisa Biomédica / Relações Interinstitucionais Tipo de estudo: Prognostic_studies País como assunto: Europa Idioma: De Ano de publicação: 2016 Tipo de documento: Article