Biomarkers are the key enablers for precision medicine. They allow for a precise disease diagnosis, selection of appropriate treatment, and assessment of disease progression. While biomarkers can be anything that is objectively measured in a patient, e.g. simple-to-measure heart rates or lab values, current research concentrates on identifying specific genes as biomarkers. Integrative biomarker detection strategies incorporate biological context into the analysis and lead to more robust and biologically meaningful biomarkers. Prior knowledge approaches are a special form of integrative analyses: They incorporate biological knowledge, e.g. gene-gene interactions or gene-diseases associations, from public knowledge bases, e.g. Gene Ontology or KEGG.
We offer master thesis topics in the scope of prior knowledge approaches, covering the implementation of novel approaches/extension of existing approaches, development of meaningful evaluation measures and strategies, and assessment of the usefulness and impact of different knowledge bases and integration levels. Focusing on multi-omics approaches for biomarker detection, i.e. integrating data from multiple molecular levels (gene expression, protein, mutation data) is also an option.
We also offer positions as student assistant/HiWi that focus on implementation-related aspects.
Contact: Cindy Perscheid