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.

Knowledge graphs have been shown to play an important role in recent knowledge mining settings, for example in the fields of life sciences or bioinformatics.Contextual information is widely used for NLP and knowledge discovery tasks, since it highly influences the exact meaning of expressions and also queries on data.The contributions of this paper are (1) an efficient approach towards interoperable data, (2) a runtime analysis of 14 realworld use cases represented by graph queries and (3) a unique view on clinical data and its application, combining methods of algorithmic optimisation, graph theory and data science.

In text mining, document clustering describes the efforts to assign unstructured documents to clusters, which in turn usually refer to topics.Clustering is widely used in science for data retrieval and organisation.In this paper we present a new graph theoretical approach to document clustering and its application on a real-world data set.We will show that the wellknown graph partition to stable sets or cliques can be generalized to pseudostable sets or pseudocliques.This allows to make a soft clustering as well as a hard clustering.We will present an integer linear programming and a greedy approach for this NP-complete problem and discuss some results on random instances and some real world data for different similarity measures.