Our junior research group focuses on developing and applying biostatistical methods to tackle diverse environmental health challenges. With the increasing availability of high-dimensional biomedical data, we pursue an interdisciplinary approach that integrates methodological statistics, scalable algorithms and biomedical expertise.
Our interdisciplinary projects span from genomics to clinical medicine and environmental epidemiology. We develop statistical methods for both fine-scale biomolecular data (e.g., single-cell RNA sequencing data) and large-scale epidemiological data (e.g., cohort studies). This integrative approach, from cellular to population levels, enables a comprehensive analysis of genetic and environmental factors, aiming to better understand disease mechanisms and improve personalized preventive and public health strategies.
Large and high-dimensional data from molecular and cohort studies come with hundreds of thousands of included cells or individuals, and thousands or millions of genetic and environmental variables. To address the need for interpretable models and uncertainty quantification, we develop scalable statistical learning methods for variable selection, estimation and prediction, including regularized regression, boosting and Bayesian approaches. A special focus is on polygenic risk score modelling to predict health outcomes, aiming to improve their currently limited generalizability across diverse populations. Additionally, we develop flexible regression modelling approaches, including spatial and temporal effects to model disease patterns in populations.
Junior group leader:
Christian Staerk
Research training group (RTG) 2624: Biostatistical methods for high-dimensional data in toxicology
The research training group (RTG) 2624 is funded by the DFG (German Research Foundation) and the time period of the second phase is 10/2025-03/2030. Participating institutions together with the IUF are the Heinrich Heine University Düsseldorf, IfADo – Leibniz Research Centre for Working Environment and Human Factors and the TU Dortmund. The aim of the RTG is the development and application of biostatistical methods for the analysis of high-dimensional data for modeling and risk assessment in toxicology. Doctoral researchers acquire knowledge in toxicology and the ability to develop and apply statistical methods for questions in pharmacological and environmental toxicology. In toxicology, innovative statistical methods are required to optimally exploit the ever-growing, heterogeneous, molecular flood of data for adequate modeling and risk prediction. In addition to conventional one-dimensional dose-response models, more complex models need to be developed. High-dimensional omics data are used in modeling both as an interaction factor for toxicological exposure and as a target.
IUF internal:
WG Krutmann
WG Rossi
WG Schikowski
WG von Mikecz
JRG Koch
JRG Sahm
National:
Moritz Berger, Central Institute of Mental Health Mannheim
Oleg Borisov, University of Freiburg
Frank Dörje, University Hospital Erlangen
Martin Fromm, Friedrich-Alexander University Erlangen-Nürnberg
Anja Hilbert, Leipzig University Hospital
Katja Ickstadt, TU Dortmund University
Ulrich Jaehde, University of Bonn
Hannah Klinkhammer, University of Marburg
Peter Krawitz, University of Bonn
Kirsten Kübler, Charité Berlin
Carlo Maj, University of Marburg
Andreas Mayr, University of Marburg
Alexandra Philipsen, University of Bonn
International:
Pariya Behrouzi, Wageningen University, Netherlands
Yosuke Tanigawa, MIT, USA
