Data Integration and Analysis
Translational research generates a continuous flow of data from a variety of sources, ranging from genotype data, such as gene expression levels, imaging data, such as tumour sizes, to phenotypic data, such as a patient's symptoms or clinical outcome. Being able to integrate these very different types of data in such a way that researchers can combine and analyse data from different studies and different sources would open up a whole new avenue of research opportunities. Completely new levels of research questions can be addressed, which in turn will contribute to new biological and clinical insights.
TraIT Data Integration and Analysis has built an environment that acts as a data warehouse and offers an extensive collection of data analytics. Data generated by the various biomedical translational studies, which is captured, stored and processed in data domain-specific tools and applications, will seamlessly integrate into and will be securely accessible through this web-based environment. A user-friendly interface ensures that researchers can query the data collection using the tools of their choice.
- Principal Investigators of the biomedical research projects
- Clinical and basic biomedical researchers
TraIT Data Integration and Analysis has opted for tranSMART as the translational research data warehousing solution.
Rather than a single software package, tranSMART is much more a software ecosystem, developed and maintained by a global user community, which can incorporate multiple data repositories and allows for a wide range of analytics to be employed.
TraIT Data Integration and Analysis has adopted a study-driven approach because of the differences between various translational research projects and the disease domains in which they operate. Several pilot projects have been executed, each focusing on loading the system with good quality data, checking the analytical functionality and adapting the system where needed. In close collaboration with the researchers involved in a particular study, suitable generic approaches have been followed and, where needed, specific features have been developed.
Work Package leader: Wim van der Linden (Philips Research)
wim.van.der.linden <at> philips.com