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Our research aims to identify the most important cellular components ("markers") characterizing biological processes such as which gene products respond most significantly to external stimuli. We use a variety of supervised and unsupervised machine learning as well graph theory-based methods to analyze and interpret complex and heterogeneous data from high-throughput experiments. In addition, the group develops internet-based database tools that allow for the efficient storage, download and management of biological measurement data. Classical bioinformatics questions such as the analysis, comparison and prediction of biological macromolecules both on the sequence as well as on the structural level are also addressed.