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Ein Forscher träufelt aus einer kleinen Pipette eine Flüssigkeit in ein Reagenzglas. Mehrere Reagenzgläser und ein Erlenmeyerkolben vorn rechts sind mit einer hellblauen, klaren Flüssigkeit gefüllt. Kopf und Schultern des Forschers sind nur unscharf im Hintergrund zu erkennen.

PREDICT - comPREhensive Data Integration for Cancer Treatment

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Project description

The central aim of the PREDICT project is to develop a software system that enables clinicians to use the large body of data on the relationships between genetic/epigenetic alterations and treatment options/success in cancer, to support (a) the rapid development of new, targeted studies whose design essentially is based on genomic features, and to (b) enable a maximally informed and structured clinical decision process.

A knowledge base will be created using advanced and innovative algorithms for knowledge extraction, semantic data integration, and biomedical text mining, and made available to the clinical oncologist through a cancer-genomic clinical workbench. Moreover, the knowledge base will be an essential tool to initiate and support highly targeted umbrella and basket trials in which experimental drugs are administered to a typically small group of patients chosen based on their mutation status.

Finally, the knowledge base will be used to develop novel algorithms to assess the effect of drugs on a patient’s tumor depending on its mutation profile.



Prof. Dr. Ulf Leser
Knowledge Management in Bioinformatics, Humboldt-Universität zu Berlin


  1. Prof. Dr. Nils Blüthgen
    Computational Modeling in Medicine, Charité Berlin
  2. Prof. Dr. med. Ulrich Keilholz
    Charité Comprehensive Cancer Center, Charité Berlin
  3. Prof. Dr. Christine Sers
    Molecular Tumor Pathology, Charité Berlin
  4. Prof. Dr. Reinhold Schäfer
    Molecular Tumor Pathology, Charité Berlin
  5. Prof. Dr. Marius Kloft
    Machine Learning, Humboldt-Universität zu Berlin

Further Information about "PREDICT".