Offering expertise of the group of Prof. Markus Scholz

Prof. Markus Scholz's team at the University of Leipzig offers extensive expertise in advanced statistical methods, particularly for analyzing high-dimensional molecular data, time series data, and sparse data sets. Their work is centered on developing and applying innovative techniques to address complex challenges in biological, medical, and clinical research.

The group's methodological expertise includes:

·        High-Dimensional Data Analysis: Specialized techniques for handling genomic, transcriptomic, proteomic, and metabolomic datasets with millions of variables, including feature selection, dimensionality reduction, and regularized regression methods.

·        Time Series Analysis: Tools for studying dynamic processes, such as disease progression or treatment responses, including temporal clustering, state-space models, and predictive modeling.

·        Sparse Data Analysis: Advanced methods to extract meaningful insights from incomplete or imbalanced datasets, such as imputation, sparse regression, and Bayesian approaches.

·        Machine Learning and AI: Integrating statistical models with machine learning algorithms to enhance predictive accuracy and discover complex patterns in data.

·        Network and Pathway Analysis: Using statistical models to explore relationships among genes, proteins, or metabolites within biological systems.

These methods are applied to a broad range of research areas, including:

·        Multi-omics integration for precision medicine.

·        Modeling disease dynamics in infectious and chronic diseases.

·        Identifying biomarkers for risk prediction and patient stratification.

With a proven track record of national and international collaborations, Prof. Scholz’s team provides cutting-edge statistical solutions to unlock the potential of complex datasets, delivering critical insights for scientific discovery and healthcare innovation.