Unify the different codding and classification of biomedical data with the use of international standards while achieving the highest level of quality and the lowest reduction in granularity. Describe data in a clinical-friendly way.
Normalize and correlate the different data types and dimensions.
Enable healthcare actors to navigate within the different types of biomedical data (phenotypic, OMICs, demographic, socioeconomic, etc.).
- Data analysis and cross-analysis
Define cohorts using clinical/medical terms in order to extract meaningful information from specific datasets and combine information from different analyses.
- Data visualization and reporting
Presenting research results, especially multidimensional data without standardized visualization forms, in a medically/clinically meaningful way.
- Artificial intelligence, machine learning and deep learning
Develop, train and validate methods to extract useful information from the data universe (e.g. predictions and estimations).
- Patient-Like-Me (PLM) tools
Translate Personalized Medicine research results from all the points above into decision support solutions for healthcare actors.
BDPM projects are following the principles of open-science, FAIR data and the GCP aiming to be easily adapted to the needs of other medical domains.