Making Sense out of Data – Providing Meaning to Models

We are experiencing tremendous technological advances, bringing a steady increase in the generation of data in biology and medicine. How can it be, that these advances do not translate into automated analyses, confident predictions and user-friendly tools? Due to the complexity of living systems, biological and medical data do not speak for themselves. We approach this challenge with the development of data analysis and modelling workflows, in which multiple heterogeneous data sources are integrated and processed by a variety of methodologies.

Not only the generation of biological and medical data has become more complicated, the analysis of data cannot be done without an interdisciplinary effort. To this end, we successfully combine a wide range of computational and mathematical approaches in integrated workflows. Our team is multidisciplinary and so is our approach to address problems in biological and medical research.

We have twenty years of experience in interdisciplinary research and training, which led to a solid track record of successfully completed projects. Our methodologies and tools are supporting the translation of biological and medical research into real world solutions and practical applications.

Our expertise at a glance:

• Network and process analyses with graph and systems theory
• Image analyses and pattern recognition using machine learning
• Sequencing analysis with deep learning
• Structural predictions employing molecular dynamics simulations
• Standardization of modelling and simulation experiments
• Omics data integration, pattern recognition and visualisation
• Workflow development with Galaxy

We analyse data and create meaningful models to improve our understanding of health and disease. Our research thereby contributes to better diagnosis, prognosis and therapy. Check our profile at www.sbi.uni-rostock.de and contact us for further information.

The success of our team is largely determined by our vision for interdisciplinary research. Our team assembles a wide range of expertise from computer science, biology, medicine, biochemistry, engineering, physics and mathematics. However, we do not think in terms of disciplines and neither do we care much about project boundaries. Out team is International, and our collaborative network is worldwide. We prefer collaborations over competition. We are fond of transparency; we trust people, not rules.

Above all, we love multidisciplinary challenges to investigate complex systems.

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