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Center for Data and Simulation Science

Prof. Dr. Beyan conducts research related to data reusability and FAIR data management, distributed analytics on sensitive data, and data-driven transformation in medicine. The aim is to achieve semantically interpretable machine actionable data and services to foster data-driven science and industry, without compromising fairness, equity, privacy, and confidentiality of individuals as well as social groups and communities. Her areas of expertise are biomedical informatics, semantic web technologies, clinical decision support, patient empowerment, research data management, and ethical and social challenges of data. As part of various European Framework Programmes and the national research data infrastructure (NFDI) and Medical Informatics Initiative projects, she led the development of the PADME platform for analytics and distributed machine learning, which supports several pilot applications and use cases in the field of medical and clinical research.

We promote medical data science and biomedical informatics as key areas for innovations in health research. We achieve this by 

  • blending cutting-edge computational methods with interdisciplinary medical research for a patient-centred approach,
  • incorporating multiple perspectives beyond medicine to social sciences and humanities to ensure real-world applicability of our outcomes and reduce translational barriers,
  • keenly offering outstanding education and impactful theses to inspire and train the next generation of researchers.

Selected publications

  1. J. Gehrmann, E. Herczog, S. Decker, O. Beyan. What prevents us from reusing medical real-world data in research. Sci Data 10, 459. (2023). doi:10.1038/s41597-023-02361-2 (Open access).
  2. M. R. Karim, T. Islam, M.Shajalal, O. Beyan, C. Lange, M. Cochez, D. Rebholz-Schuhmann, S. Decker. Explainable AI for Bioinformatics: Methods, Tools and Applications. Brief Bioinform, 24(5):bbad236. (2023). doi: 10.1093/bib/bbad236 (Open access).
  3. Z. Boukhers, C. Lange, O. Beyan. Enhancing Data Space Semantic Interoperability through Machine Learning: a Visionary Perspective. In Companion Proceedings of the ACM Web Conference, pp. 1462-1467. (2023). doi: 10.1145/3543873.3587658 (Open access).
  4. M. Jaberansary, M. Maia, Y. Ucer Yediel, O. Beyan, T. Kirsten. Analyzing Distributed Medical Data in FAIR Data Spaces. WWW '23 Companion: Companion Proceedings of the ACM Web Conference, pp. 1480-1484. (2023). doi: 10.1145/3543873.3587663 (Open access).
  5. S. Welten, Y. Mou, L. Neumann, M. Jaberansary, Y. Ucer Yediel, T. Kirsten, S. Decker, O. Beyan. A Privacy-Preserving Distributed Analytics Platform for Health Care Data. Methods of Information in Medicine. (2022) doi: 10.1055/s-0041-1740564 (Open access).
  6. M. R. Karim, O. Beyan, A. Zappa, I. G. Costa, D. Rebholz-Schuhmann, M. Cochez, S. Decker. Deep learning-based clustering approaches for bioinformatics. Briefings in bioinformatics. 22(1), 393-415. (2021). doi: 10.1093/bib/bbz170 (Open access).
  7. O. Beyan, A. Choudhury, J. Van Soest, O. Kohlbacher, L. Zimmermann, H. Stenzhorn, ..., A. Dekker. Distributed analytics on sensitive medical data: the personal health train. Data Intelligence, 2(1-2), 96-107. (2020). doi: 10.1162/dint_a_00032 (Open access).