On the following pages you see a current overview. Questions regarding the research activities of our department can be directed to research@bioinf.med.uni-goettingen.de at any time.


In this project we develop methods and tools to extract information relevant to the patient and present it to the clinician.

CRU 5002

Desciphering genome dynamics for subtype specific therapie in pancreatic cancer.


Performing a comprehensive characterization of the tissue micro- and mycobiome of young CRC patients, identifying the oncogenic microbiome signature and understanding its influence on oncogenic signaling resulting in tumor development and progression. 

AutoBuSTeD stained pipeline steps


This project develops the hardware and image analysis software for an automated bubble sweat test diagnostics system developed with partners from the MHH in Hannover.


This project develops a generic software platform / toolbox to handle and annotate chemical structure libraries in a way that clinical researchers can organize chemical structures annotating a biological system.


We aim at developing a usable common standard for the required experiments, the automated analysis via software and providing the experimental hardware setups for an easy dissemination of the technique to other sites.


FDLP - Federated Learning in Lymphoma Pathology: Infrastructure, Models, Extension Algorithms, Detection of High-Risk Patients.

This poject supports the development of federated machine learning methods to develop models that enable the prediction of prognostic subtypes.

TRR 274 - Checkpoints of Central Nervous System Recovery

Our aim is to define the immunological, glial and neuronal checkpoints that faithfully predict the outcome of CNS injuries, and to develop intervention strategies targeting these checkpoints that guide an injured CNS tissue towards recovery.


The aim of the project is to develop FAIrPaCT, a software system supported by federated artificial intelligence.


Digital Tissue Deconvolution (DTD) - Expression profiles of complex tissue are used to digitally back-calculate the cellular composition. 


The Chronic Disease Nephrologist's App (CKDNapp) is designed as a clinical decision support system to assist the practising nephrologist in the management of patients with chronic kidney didease. 


An interdisciplinary project with the aim to develop new and innovative methods for individualizing the treatment of cardiovascular diseases.

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