Integrative Data Semantics through a Model-enabled Data Stewardship
Philipp Wegner, Sebastian Schaaf, Mischa Uebachs, Daniel, Domingo-Fern\'andez, Yasamin Salimi, Stephan Gebel, Astghik Sargsyan, Colin, Birkenbihl, Stephan Springstubbe, Thomas Klockgether, Juliane Fluck, Martin, Hofmann-Apitius, and Alpha Tom Kodamullil

TL;DR
This paper introduces the Data Steward Tool (DST), which facilitates semi-automatic semantic integration of heterogeneous clinical datasets into a unified data model, enhancing interoperability in dementia research.
Contribution
The paper presents a novel semi-automatic tool and a disease-specific data model that improve data integration and interoperability across diverse clinical datasets.
Findings
DST enables semantic integration of clinical data.
The dementia-specific data model covers 277 variables.
Interoperability between multiple datasets is achieved.
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 aetiology. 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…
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Taxonomy
TopicsBiomedical Text Mining and Ontologies · Semantic Web and Ontologies · Genomics and Rare Diseases
MethodsDynamic Sparse Training
