Ontology highlight
ABSTRACT: Motivation
The importance of clinical data in understanding the pathophysiology of complex disorders has prompted the launch of multiple initiatives designed to generate patient-level data from various modalities. While these studies can reveal important findings relevant to the disease, each study captures different yet complementary aspects and modalities which, when combined, generate a more comprehensive picture of disease etiology. However, achieving this requires a global integration of data across studies, which proves to be challenging given the lack of interoperability of cohort datasets.Results
Here, we present the Data Steward Tool (DST), an application that allows for semi-automatic semantic integration of clinical data into ontologies and global data models and data standards. We demonstrate the applicability of the tool in the field of dementia research by establishing a Clinical Data Model (CDM) in this domain. The CDM currently consists of 277 common variables covering demographics (e.g. age and gender), diagnostics, neuropsychological tests and biomarker measurements. The DST combined with this disease-specific data model shows how interoperability between multiple, heterogeneous dementia datasets can be achieved.Availability and implementation
The DST source code and Docker images are respectively available at https://github.com/SCAI-BIO/data-steward and https://hub.docker.com/r/phwegner/data-steward. Furthermore, the DST is hosted at https://data-steward.bio.scai.fraunhofer.de/data-steward.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Wegner P
PROVIDER: S-EPMC9344835 | biostudies-literature | 2022 Aug
REPOSITORIES: biostudies-literature
Wegner Philipp P Schaaf Sebastian S Uebachs Mischa M Domingo-Fernández Daniel D Salimi Yasamin Y Gebel Stephan S Sargsyan Astghik A Birkenbihl Colin C Springstubbe Stephan S Klockgether Thomas T Fluck Juliane J Hofmann-Apitius Martin M Kodamullil Alpha Tom AT
Bioinformatics (Oxford, England) 20220801 15
<h4>Motivation</h4>The importance of clinical data in understanding the pathophysiology of complex disorders has prompted the launch of multiple initiatives designed to generate patient-level data from various modalities. While these studies can reveal important findings relevant to the disease, each study captures different yet complementary aspects and modalities which, when combined, generate a more comprehensive picture of disease etiology. However, achieving this requires a global integrati ...[more]