23 research outputs found

    Datenintegration in biomedizinischen Forschungsverbünden auf Basis von serviceorientierten Architekturen

    Get PDF
    In biomedizinischen Forschungsverbünden besteht der Bedarf, Forschungsdaten innerhalb des Verbundes und darüber hinaus gemeinsam zu nutzen. Hierzu wird zunächst ein Anforderungsmodell erstellt, das anschließend konsolidiert und abstrahiert wird. Daraus ergibt sich ein Referenzmodell für Anforderungen, welches anderen Forschungsverbünden als Grundlage für die beschleunigte Erstellung eines eigenen SOA-Systems dienen kann. Zum Referenzmodell wird weiterhin eine konkrete Instanz als Anforderungsmodell für den durch die Deutsche Forschungsgemeinschaft (DFG) geförderten geförderten Sonderforschungsbereich/Transregio 77 „Leberkrebs–von der molekularen Pathogenese zur zielgerichteten Therapie“ beschrieben. Aus dem Anforderungsmodell wird ein IT-Architekturmodell für den Verbund abgeleitet, welches aus Komponentenmodell, Verteilungsmodell und der Sicherheitsarchitektur besteht. Die Architektur wird unter Verwendung des Cancer Biomedical Informatics Grid (caBIG) umgesetzt. Dabei werden die in den Projekten anfallenden Daten in Datendienste umgewandelt und so für den Zugriff in einer SOA bereitgestellt. Durch die Datendienste kann die Anforderung der Projekte, die Kontrolle über die eigenen Daten zu behalten, weitgehend erfüllt werden: Die Dienste können mit individuellen Zugriffsberechtigungen versehen und dezentral betrieben werden, bei Bedarf auch im Verantwortungsbereich der Projekte selbst. Der Zugriff auf das System erfolgt mittels eines Webbrowsers, mit dem sich die Mitarbeiter des Verbundes unter Verwendung einer individuellen Zugangskennung an einem zentralen Portal anmelden. Zum einfachen und sicheren Austausch von Dokumenten innerhalb des Verbundes wird ein Dokumentenmanagementsystem in die SOA eingebunden. Um die Forschungsdaten aus verschiedenen Quellen auch auf semantischer Ebene integrieren zu können, werden Metadatensysteme entwickelt. Hierzu wird ein kontrolliertes Vokabular erstellt, das mit der hierfür entwickelten Methode aus den von den Projekten verwendeten Terminologien gewonnen wird. Die so gesammelten Begriffe werden mit standardisierten Vokabularien aus dem Unified Medical Language System (UMLS) abgeglichen. Hierfür wird ein Software-Werkzeug erstellt, das diesen Abgleich unterstützt. Des Weiteren hat sich im Rahmen dieser Arbeit herausgestellt, dass keine Ontologie existiert, um die in der biomedizinischen Forschung häufig verwendeten Zelllinien einschließlich ihrer Wachstumsbedingungen umfassend abzubilden. Daher wird mit der Cell Culture Ontology (CCONT) eine neue Ontologie für Zelllinien entwickelt. Dabei wird Wert darauf gelegt, bereits etablierte Ontologien dieses Bereichs soweit wie möglich zu integrieren. Somit wird hier eine vollständige IT-Architektur auf der Basis einer SOA zum Austausch und zur Integration von Forschungsdaten innerhalb von Forschungsverbünden beschrieben. Das Referenzmodell für Anforderungen, die IT-Architektur und die Metadatenspezifikationen stehen für andere Forschungsverbünde und darüber hinaus als Grundlagen für eigene Entwicklungen zur Verfügung. Gleiches gilt für die entwickelten Software-Werkzeuge zum UMLS-Abgleich von Vokabularen und zur automatisierten Modellerstellung für caBIG-Datendienste

    Information management for enabling systems medicine

    No full text
    Systems medicine is a data-oriented approach in research and clinical practice to support study and treatment of complex diseases. It relies on well-defined information management processes providing comprehensive and up to date information as basis for electronic decision support. The authors suggest a three-layer information technology (IT) architecture for systems medicine and a cyclic data management approach including a knowledge base that is dynamically updated by extract, transform, and load (ETL) procedures. Decision support is suggested as case-based and rule-based components. Results are presented via a user interface to acknowledging clinical requirements in terms of time and complexity. The systems medicine application was implemented as a prototype

    Requirements for data integration platforms in biomedical research networks: a reference model

    No full text
    Biomedical research networks need to integrate research data among their members and with external partners. To support such data sharing activities, an adequate information technology infrastructure is necessary. To facilitate the establishment of such an infrastructure, we developed a reference model for the requirements. The reference model consists of five reference goals and 15 reference requirements. Using the Unified Modeling Language, the goals and requirements are set into relation to each other. In addition, all goals and requirements are described textually in tables. This reference model can be used by research networks as a basis for a resource efficient acquisition of their project specific requirements. Furthermore, a concrete instance of the reference model is described for a research network on liver cancer. The reference model is transferred into a requirements model of the specific network. Based on this concrete requirements model, a service-oriented information technology architecture is derived and also described in this paper

    Graph-Representation of Patient Data: a Systematic Literature Review

    No full text
    Graph theory is a well-established theory with many methods used in mathematics to study graph structures. In the field of medicine, electronic health records (EHR) are commonly used to store and analyze patient data. Consequently, it seems straight-forward to perform research on modeling EHR data as graphs. This systematic literature review aims to investigate the frontiers of the current research in the field of graphs representing and processing patient data. We want to show, which areas of research in this context need further investigation. The databases MEDLINE, Web of Science, IEEE Xplore and ACM digital library were queried by using the search terms health record, graph and related terms. Based on the 'Preferred Reporting Items for Systematic Reviews and Meta-Analysis' (PRISMA) statement guidelines the articles were screened and evaluated using full-text analysis. Eleven out of 383 articles found in systematic literature review were finally included for analysis in this literature review. Most of them use graphs to represent temporal relations, often representing the connection among laboratory data points. Only two papers report that the graph data were further processed by comparing the patient graphs using similarity measurements. Graphs representing individual patients are hardly used in research context, only eleven papers considered such kind of graphs in their investigations. The potential of graph theoretical algorithms, which are already well established, could help increasing this research field, but currently there are too few papers to estimate how this area of research will develop. Altogether, the use of such patient graphs could be a promising technique to develop decision support systems for diagnosis, medication or therapy of patients using similarity measurements or different kinds of analysis

    On the Ontology Based Representation of Cell Lines

    Get PDF
    <div><p>Cell lines are frequently used as highly standardized and reproducible <em>in vitro</em> models for biomedical analyses and assays. Cell lines are distributed by cell banks that operate databases describing their products. However, the description of the cell lines' properties are not standardized across different cell banks. Existing cell line-related ontologies mostly focus on the description of the cell lines' names, but do not cover aspects like the origin or optimal growth conditions. The objective of this work is to develop an ontology that allows for a more comprehensive description of cell lines and their metadata, which should cover the data elements provided by cell banks. This will provide the basis for the standardized annotation of cell lines and corresponding assays in biomedical research. In addition, the ontology will be the foundation for automated evaluation of such assays and their respective protocols in the future. To accomplish this, a broad range of cell bank databases as well as existing ontologies were analyzed in a comprehensive manner. We identified existing ontologies capable of covering different aspects of the cell line domain. However, not all data fields derived from the cell banks' databases could be mapped to existing ontologies. As a result, we created a new ontology called <em>cell culture ontology (CCONT)</em> integrating existing ontologies where possible. CCONT provides classes from the areas of cell line identification, origin, cell line properties, propagation and tests performed.</p> </div

    Number of chemical compounds for each role in CCONT.

    No full text
    <p>Chemical compounds play different roles for the growth of cell lines. In column two we show how many compounds are necessary for describing a specific role. Column three shows the respective number of roles already present in EFO.</p

    Sample data fields in cell bank databases.

    No full text
    <p>The same information is attributed to different data fields in the cell banks we reviewed. This table shows how the two sample terms ‘medium’ and ‘organ’ are handled in the respective databases. The fields marked by * are free text fields. They are also used for other types of data.</p

    Ontology evaluation techniques (Obrst et al. [16]).

    No full text
    <p>This table summarizes the evaluation techniques suggested by Obrst et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048584#pone.0048584-Obrst1" target="_blank">[16]</a> for use with ontologies in life sciences. The techniques one to three were applied when evaluating CCONT.</p
    corecore