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.

MTB-Report

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

AutoBuSTeD

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.

CandActCFTR

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.

MyPathSem

MyPathSem

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. 

MATCH

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.

CKDNapp

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

DTD

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

PerMiCCion

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. 

FAIrPaCT

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

FDLP

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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