What we do
DIFUTURE Tübingen joined the GO:FAIR PHT Implementation Network in 2018 as a milestone for distributed FAIR analysis. We aim to deploy a possible solution to achieve the defined goals from the PHT manifesto.
We develop proof-of-concepts, fully open source accessible, to demonstrate how modern cloud techniques can be leveraged for complex distributed, privacy-preserving medical data analytics.
Learn more about
our current work
We work on several use cases with different partners to evaluate and demonstrate our development.
Our current main focus is:
- image and genome analysis
A PHT Tübingen update presented by Lukas Zimmermann. Presented at International FAIR Convergence Symposium 2020.
A shared status update with Aachen presented by Marius Herr, Sascha Welten and Oya Beyan with the architecture from Tübingen, Demo in Aachen and next steps. Presented at the MII workshop for distributed analysis in Germany.
A status update presented by Marius Herr of the recent progress at Tübingen. Presented at the DIFUTURE symposium 2019 in Tübingen.
The first proof-of-concept presentation by Oliver Kohlbacher of our PHT architecture, workflow and train-API. Presented at the DIFUTURE symposium 2018 in Munich, the GO FAIR meeting in Leiden and Berlin .
Our code is fully open source and can be accessed at gitlab.com
Lukas (MSc in Bioinformatics), works as cloud scientist and is technical lead of the Personal Health Train Tübingen. He is part of the DIFUTURE informatics Team.
Michael studied in his B.Sc cognitive science and will finish his M.Sc in computer science soon. He works as research assistant at the Institute of Translational Bioinformatics (TBI) and works on: security implementation and enabling new methods using the Personal Health Train.
Florian studied in his B.Sc computer science and in his M.Sc in medical informatics. He works as research assistant at the Institute of Translational Bioinformatics (TBI) and works on: automatic detection of malicious trains and enabling new methods using the Personal Health Train.
You are interested to contribute to the Personal Health Train project in one of the following ways:
- code development
- concepts to extend or enable different methods
- research questions you want to answer with de-central analysis