Research topics

Our research aims at deepening the understanding of genome biology and advancing personalized medicine. Besides our own fields of interest we engage in collaborative research projects with the department of Human Genetics, the Excellence Cluster Immunosensation and with basically any lab on the campus that has challenging high dimensional data. In most of our projects we are using artificial intelligence in the analysis of big biomedical data sets and we are trying to understand what the machines are doing.

GPI anchor deficiencies

Identification of pathogenic sequence variants


PEDIA

Prioritization of Exome Data by Image Analysis


Radar

Study on possible DNA damage in descendants of radar technicians


FlowCat

Automated classification of B-cell neoplasms with AI


Fundus2Sex

A Computational Approach for Interpretable AI


Gestaltmatcher

GestaltMatch: breaking the limits of rare disease matching using facial phenotypic descriptors


GenRisk

GenRisk is a python package that processes genetic data to generate both gene-based burden scores and PRS for association tests and the development of prediction models


snpboost - Boosting Polygenic Risk Scores

To fit polygenic risk scores (PRS) directly on individual level genotype data, we developed the adapted statistical boosting framework snpboost which is implemented in R.

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