Titlebook: Data Integration in the Life Sciences; 10th International C Helena Galhardas,Erhard Rahm Conference proceedings 2014 Springer International

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Annotation-Based Feature Extraction from Sets of SBML Modelss from three frequently used ontologies for SBML models, namely Gene Ontology, ChEBI and SBO. We find that three of the four tested methods are suitable to determine characteristic features for model sets. The selected features vary depending on the underlying model set, and they are also specific t

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Towards Visualizing the Alignment of Large Biomedical Ontologiese matched ontologies and the mappings between them. This strategy decreases the need for computational resources and improves the visualization experience, by letting the user focus on selected areas of the alignment.

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Data Integration in the Life Sciences978-3-319-08590-6Series ISSN 0302-9743 Series E-ISSN 1611-3349

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https://doi.org/10.1007/978-1-4842-8876-4mic, proteomic, imaging, and clinical data. Yet despite the proliferation of data-driven methods, tools to support the integration and management of large collections of data for purposes of data driven discovery are scarce, leaving scientists with ad hoc and inefficient processes. The scientific pr

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Cloud Native Architecture and Designensive overview of various data sources that can be combined in order to power up a study is a key factor in the design. Clinical data stored in health registries and biobank data in research projects are commonly provisioned in different database systems and governed by separate organizations, maki

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Microservices Architecture and Designry lab desk alike. In this paper, we report on experiences from a decade of data management in a plant research institute. We explain the switch from personally managed files and heterogeneous information systems towards a centrally organised storage management. In particular, we discuss lessons tha

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