Biobank sustainability: current status and future prospects

Biobanks play an important role in biomedical research that aims to understand cellular and molecular mechanisms underlying the development of diseases, and to improve interventions for human health. Despite financial support from mixed public funding streams, long-term sustainability of public biobanks remains a major concern. Review of the literature demonstrates that total cost-recovery strategies, as well as commercialization of research results or derived products, may not represent the best way to reach and maintain sustainability. Public biobanks require support by long-term investment and commitment from public and governmental sources, as well as support from industrial users. In this regard, this study suggests strategies to improve long-term sustainability, such as sample-sharing and biobank consolidation to reduce unit costs, embedding public biobanks in health care systems, and working to implement global funding mechanisms

An MDA approach for developing Secure OLAP applications: metamodels and transformations

Decision makers query enterprise information stored in Data Warehouses (DW) by using tools (such as On-Line Analytical Processing (OLAP) tools) which employ specific views or cubes from the corporate DW or Data Marts, based on multidimensional modelling. Since the information managed is critical, security constraints have to be correctly established in order to avoid unauthorized access. In previous work we defined a Model-Driven based approach for developing a secure DW repository by following a relational approach. Nevertheless, it is also important to define security constraints in the metadata layer that connects the DW repository with the OLAP tools; that is, over the same multidimensional structures that end users manage. This paper incorporates a proposal for developing secure OLAP applications within our previous approach: it improves a UML profile for conceptual modelling; it defines a logical metamodel for OLAP applications; and it defines and implements transformations from conceptual to logical models, as well as from logical models to secure implementation in a specific OLAP tool (SQL Server Analysis Services).This research is part of the following projects: SIGMA-CC (TIN2012-36904), GEODAS-BC (TIN2012-37493-C01) and GEODAS-BI (TIN2012-37493-C03) funded by the Ministerio de Economía y Competitividad and Fondo Europeo de Desarrollo Regional FEDER. SERENIDAD (PEII11-037-7035) and MOTERO (PEII11- 0399-9449) funded by the Consejería de Educación, Ciencia y Cultura de la Junta de Comunidades de Castilla La Mancha, and Fondo Europeo de Desarrollo Regional FEDER

Enhancing reuse of data and biological material in medical research : from FAIR to FAIR-Health

The known challenge of underutilization of data and biological material from biorepositories as potential resources formedical research has been the focus of discussion for over a decade. Recently developed guidelines for improved data availability and reusability—entitled FAIR Principles (Findability, Accessibility, Interoperability, and Reusability)—are likely to address only parts of the problem. In this article,we argue that biologicalmaterial and data should be viewed as a unified resource. This approach would facilitate access to complete provenance information, which is a prerequisite for reproducibility and meaningful integration of the data. A unified view also allows for optimization of long-term storage strategies, as demonstrated in the case of biobanks.Wepropose an extension of the FAIR Principles to include the following additional components: (1) quality aspects related to research reproducibility and meaningful reuse of the data, (2) incentives to stimulate effective enrichment of data sets and biological material collections and its reuse on all levels, and (3) privacy-respecting approaches for working with the human material and data. These FAIR-Health principles should then be applied to both the biological material and data. We also propose the development of common guidelines for cloud architectures, due to the unprecedented growth of volume and breadth of medical data generation, as well as the associated need to process the data efficiently.peer-reviewe

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La recherche biomédicale connaît depuis le début du siècle un bouleversement de grande ampleur avec l’avènement de technologies à grand débit (les -omiques) appliquées à la biologie et associées à des approches biologiques, moléculaires ou aux techniques d’imagerie. Cette révolution méthodologique s’appuie sur l’analyse d’échantillons biologiques prélevés sur les patients puis conservés dans des biobanques. L’intégration des données massives obtenues par ces différentes technologies et leur analyse devrait permettre d’accroître nos connaissances des mécanismes complexes des pathologies humaines et une meilleure stratification des patients selon une nomenclature génétique ou moléculaire. L’accroissement exponentiel des données générées et leur complexité nécessitent cependant la mise en place d’infrastructures adaptées, de nouvelles modalités d’accès et d’échanges de ces données ainsi qu’une organisation optimisée des biobanques afin d’intégrer de nouvelles disciplines adaptées à l’analyse de ces données