ABSTRACT
Biomarker discovery and validation projects require an integrated translational suite of software tools that enable correlation between clinical phenotype data on the one hand, and readout data for the underlying biology on the other hand. The latter is increasingly well organized through data pipelines and analysis tools that are specifically targeted at the methodology used such as clinical imaging, genomics, and proteomics. In contrast, the availability of robust clinical phenotypic data often proves to be the weak link in biomarker studies. Outcome data from clinical practice consistently fails to meet the criteria set by the FAIR principles that data should be findable, accessible, interoperable, and reusable. Modern software tooling and standardization initiatives offer a great promise to change this situation for the better over the coming years. This section provides a high-level overview of the available resources (biobanks, data registries), data integration and collaboration solutions, and data acquisition pipelines.
