Michael Schroeder Group
Computational Drug Repositioning with Networks, Structures, Text & Ontologies
Developing a drug costs in the order of a billion USD and takes some 10 years. Drug repositioning tries to reduce cost and time by applying drugs that are already on the market to new uses. With the avalanche of publicly available data on compounds, genes, proteins, and diseases and their relationships, there is a tremendous need for computational approaches to organize and analyze this data and finally predict novel uses. In our group we are specifically focusing on developing algorithms and analysis pipelines using networks, protein structures, text-mining and ontologies for this purpose. We currently focus on pancreas cancer as application, where we have a wealth of experimental data from collaborations with the medical faculty.
Future Projects and Goals
We currently focus on prediction of pancreas cancer drugs and biomarkers using graph algorithms, protein structures, and text-mining. We experimentally validate predictions with simple assay.