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1005 Publications visible to you, out of a total of 1005
Ontology-based Retrieval of Scientific Data in LIFE
LIFE - Leipzig Research Center for Civilization Diseases

Abstract (Expand)

LIFE is an epidemiological study determining thousands of Leipzig inhabitants with a wide spectrum of interviews, questionnaires, and medical investigations. The heterogeneous data are centrally … integrated into a research database and are analyzed by specific analysis projects. To semantically describe the large set of data, we have developed an ontological framework. Applicants of analysis projects and other interested people can use the LIFE Investigation Ontology (LIO) as central part of the framework to get insights, which kind of data is collected in LIFE. Moreover, we use the framework to generate queries over the collected scientific data in order to retrieve data as requested by each analysis project. A query generator transforms the ontological specifications using LIO to database queries which are implemented as project-specific database views. Since the requested data is typically complex, a manual query specification would be very timeconsuming, error-prone, and is, therefore, unsuitable in this large project. We present the approach, overview LIO and show query formulation and transformation. Our approach runs in production mode for two years in LIFE.

Authors: Toralf Kirsten, A. Uciteli

Date Published: 2015

Publication Type: Not specified

Analysis of Large-Scale OMIC Data Using Self Organizing Maps
LHA - Leipzig Health Atlas

Abstract (Expand)

The data produced by high-throughput bioanalytics is usually given as a feature matrix of dimension N x M (see Figure 1) where N is the number of features measured per sample and M is the number of … samples referring, e.g., to different treatments, time points or individuals. As a convention, each row of the matrix will be termed profile of the respective feature. The columns on the other hand will be termed states referring to each of the conditions studied. In general, the number of features can range from several thousands to millions, depending on the experimental screening technique used. Typically, this number largely exceeds the number of states studied, i.e. N>>M. SOM machine learning aims at reducing the number of relevant features by grouping the input data into clusters of appropriate size, and thus to transform the matrix of input data into a matrix of so-called meta-data with a reduced number of meta-features, K<<N (Figure 1a and b). In other words, SOM aims at mapping the space of the high-dimensional input data onto meta-data space of reduced dimensionality.

Authors: Hans Binder, Henry Löffler-Wirth

Date Published: 2015

Publication Type: Not specified

Monitoring Disease Progression and Therapeutic Response in a Disseminated Tumor Model for Non-Hodgkin Lymphoma by Bioluminescence Imaging
Genetical Statistics and Systems Biology

Abstract (Expand)

Xenograft tumor models are widely studied in cancer research. Our aim was to establish and apply a model for aggressive CD20-positive B-cell non-Hodgkin lymphomas, enabling us to monitor tumor growth … and shrinkage in a noninvasive manner. By stably transfecting a luciferase expression vector, we created two bioluminescent human non-Hodgkin lymphoma cell lines, Jeko1(luci) and OCI-Ly3(luci), that are CD20 positive, a prerequisite to studying rituximab, a chimeric anti-CD20 antibody. To investigate the therapy response in vivo, we established a disseminated xenograft tumor model injecting these cell lines in NOD/SCID mice. We observed a close correlation of bioluminescence intensity and tumor burden, allowing us to monitor therapy response in the living animal. Cyclophosphamide reduced tumor burden in mice injected with either cell line in a dose-dependent manner. Rituximab alone was effective in OCI-Ly3(luci)-injected mice and acted additively in combination with cyclophosphamide. In contrast, it improved the therapeutic outcome of Jeko1(luci)-injected mice only in combination with cyclophosphamide. We conclude that well-established bioluminescence imaging is a valuable tool in disseminated xenograft tumor models. Our model can be translated to other cell lines and used to examine new therapeutic agents and schedules.

Authors: Margarethe Köberle, Kristin Müller, Manja Kamprad, Friedemann Horn, Markus Scholz

Date Published: 2015

Publication Type: Journal article

Entwicklung und Einsatz einer Domänenontologie des Informationsmanagements im Krankenhaus.
Management of health information systems

Abstract

Not specified

Author: Jahn F Schaaf M

Date Published: 2015

Publication Type: InCollection

Tracking Study Patients in a University Hospital Information System
Management of health information systems

Abstract

Not specified

Authors: Sebastian Stäubert, Kirsti Meinel, Frank Meineke, Matthias Löbe, Alfred Winter

Date Published: 2015

Publication Type: InProceedings

Elektronische Arzneimittelverordnung – Autopilot in der Arzneimitteltherapie? Literatur- und Datenanalyse
Management of health information systems

Abstract

Not specified

Author: Martina Patrizia Neininger Sten Schubert

Date Published: 2015

Publication Type: Journal article

Good Medicine and Good Healthcare Demand Good Information (Systems)
Management of health information systems

Abstract (Expand)

The demand for evidence-based health informatics and benchmarking of ’good’ information systems in health care gives an opportunity to continue reporting on recent papers in the German journal GMS … Medical Informatics, Biometry and Epidemiology (MIBE) here. The publications in focus deal with a comparison of benchmarking initiatives in German-speaking countries, use of communication standards in telemonitoring scenarios, the estimation of national cancer incidence rates and modifications of parametric tests. Furthermore papers in this issue of MIM are introduced which originally have been presented at the Annual Conference of the German Society of Medical Informatics, Biometry and Epidemiology. They deal as well with evidence and evaluation of ’good’ information systems but also with data harmonization, surveillance in obstetrics, adaptive designs and parametrical testing in statistical analysis, patient registries and signal processing.   The demand for evidence-based health informatics and benchmarking of ’good’ information systems in health care gives an opportunity to continue reporting on recent papers in the German journal GMS Medical Informatics, Biometry and Epidemiology (MIBE) here. The publications in focus deal with a comparison of benchmarking initiatives in German-speaking countries, use of communication standards in telemonitoring scenarios, the estimation of national cancer incidence rates and modifications of parametric tests. Furthermore papers in this issue of MIM are introduced which originally have been presented at the Annual Conference of the German Society of Medical Informatics, Biometry and Epidemiology. They deal as well with evidence and evaluation of ’good’ information systems but also with data harmonization, surveillance in obstetrics, adaptive designs and parametrical testing in statistical analysis, patient registries and signal processing. //  The demand for evidence-based health informatics and benchmarking of ’good’ information systems in health care gives an opportunity to continue reporting on recent papers in the German journal GMS Medical Informatics, Biometry and Epidemiology (MIBE) here. The publications in focus deal with a comparison of benchmarking initiatives in German-speaking countries, use of communication standards in telemonitoring scenarios, the estimation of national cancer incidence rates and modifications of parametric tests. Furthermore papers in this issue of MIM are introduced which originally have been presented at the Annual Conference of the German Society of Medical Informatics, Biometry and Epidemiology. They deal as well with evidence and evaluation of ’good’ information systems but also with data harmonization, surveillance in obstetrics, adaptive designs and parametrical testing in statistical analysis, patient registries and signal processing.

Authors: Alfred Winter, R-D Hilgers, R. Hofestädt, Ursula Hübner, Petra Knaup-Gregori, C. Ose, C. Schmoor, A. Timmer, D. Wege

Date Published: 2015

Publication Type: Journal article

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