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Research Projects

REGRESSION: High-dimensional regression methods for gene-exposure interactions

The central goal in this project area is the modelling of health indicators with modern regression methods. The influence of high-dimensional omics data on the relationship between toxicological exposure and health is investigated. The models considered comprise classical linear and logistic regression as well as modern procedures such as logic regression, regularised procedures and generalised additive models. For selecting the most important influential variables and interactions, methods like component-wise boosting will be generalised, genetic risk scores will be formulated, and sampling techniques will be adapted.

PREDICTION: Statistical modelling of omics data depending on exposure and dose

A central topic in this project area is the estimation of the minimum effective dose of a compound, using high-dimensional omics data as a surrogate for effects on health. Statistical learning methods are used to select relevant genetic markers. In addition, multi-level statistical modelling approaches will be developed for the simultaneous consideration of different doses and exposure times, and methods for deriving optimal experimental designs for such models.

INTEGRATION: Data integration with omics data and additional information

The aim of this project area is to improve toxicological risk prediction and the differentiation of toxicological effects through appropriate integration of data, models, and results. For data integration, various molecular data sources will be combined and additional biological knowledge will be integrated into the modelling. Integrative modelling approaches will build upon models comprising those developed in the research areas regression and prediction.