About Computational BioMedicine Laboratory
The mission of the Computational BioMedicine Laboratory (CBML) is to develop novel ICT technologies in the wider context of personalized, predictive and preventive medicine aiming at:
- the optimal management of chronic diseases (such as diabetes, cardiovascular disease) and the development of clinical decision support systems,
- the optimization of diagnosis and treatment through the use of novel medical imaging analysis tools and predictive models,
- the integration of multi-level biomedical data for supporting postgenomic clinical trials,
- the integration of in vitro, in vivo and clinical data with mathematical and computational approaches to better understand cancer complexity and progression,
- the implementation of well-established in silico methods and tools towards novel approaches that could be incorporated in the medical clinical research,
- the understanding of spatio-temporal neuronal dynamics of the brain reflecting different perceptual, motor or cognitive states that may be indicative of a wider range of cognitive functions or brain pathologies,
- the semantic interoperability of biomedical data tools and models for enhancing biomedical knowledge discovery.
- the theoretical and algorithmic research in areas of bioinformatics to improve the state- of-the-art and invent solutions to new problems,
- the applications of the state-of-the-art and best-practices in computational methods on specific biological problems with the intent to discover new biomedical knowledge, and support Translation BioMedical methodologies.
These active research directions are supported by several EC and national grants as well as a number of strategic clinical collaborations ensuring that the research output is driven from the actual clinical requirements and be translated to the clinical setting. Our mission in conclusion, is to contribute through our technology to the expected transformation of medicine making disease more preventable and treatment outcome more predictable, effective and personalized.