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.

AutoBuSTeD stained pipeline steps


This project develops the hardware and image analysis software for an automated bubble sweat test diagnostics system developed as a partner with the UCLouvain in Brussels and 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.



The aim of this project is reducing the gap between patient centered routine documentation and ontology-driven pathway and gene annotation resulting in a seamless data-flow from single patient data to Systems Medicine. 


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

CRU 5002

Desciphering genome dynamics for subtype specific therapie in pancreatic cancer.


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. 

Insights into the laboratory


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


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. 


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


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.

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