Digital Biology & Digital Medicine
The increasing amount of data that are being acquired, stored, and processed in the life sciences and health sector makes the development of new information technologies one of the key factors for advancing the current state of knowledge in biomedical and health research. We develop such technologies for extracting and exploiting information from clinical and biological data, e.g., from omics data and related repositories, from medical and biomedical images, or from population-wide health records. We build models of biological, physiological, or anatomical function using techniques from machine learning and mathematical modeling, together with evidence from large data bases. These models are used to advance our knowledge about biological processes, but also to inform decisions in clinical routine, for example, in the interpretation of diagnostic images and health records, or when treating patients in the operating theatre.
Julien Gagneur, Prof. Dr.
Helmut Krcmar, Prof. Dr. rer. oec.
- TUM-IAS Focus: Image-based Biomedical Modelling
- TUM-IAS Focus: Functional Metagenomics (incl. TUM-IAS Fellow: Prof. Yana Bromberg, Rutgers)
- Coordination of study section of Bioinformatics shared between TUM, Ludwig-Maximilians-Universität (LMU), Max-Planck-Gesellschaft (MPG), Helmholtz-Gesellschaft
- Coordination of Digital Medicine between Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, Universität Augsburg, Ostbayerische Technische Hochschule (OTH) Amberg-Weiden
- Participation in Bundesministerium für Bildung und Forschung (BMBF) call for Medical Informatics
- Leading role in activity for Digital Medicine in Zentrum Digitalisierung Bayern (ZD.B)
- E:Med junior research alliance mitoMics (Prof. J. Gagneur)
The project team develops and validates systematic approaches to infer the molecular bases of mitochondrial diseases in individual patients by combining genetics, functional genomics, and statistical causal inference.
- DFG Projekt: "Whole body image analysis for diagnosing patients with monoclonal plasma cell disorders"
The project team develops technologies for the analysis of multimodal whole body images that scale to large data sets, and probabilistic tumor evolution models that summarize disease progression at the population scale.