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 based. 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.


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  • Rieke, D. T., Lamping, M., Klauschen, F., … Keilholz, U. (2018). “Efficacy of a structured workflow for the interpretation of comprehensive genomic analysis data in clinical routine.” J Clin Oncol
  • Rieke, D. T., Lamping M., … , Keilholz U. (2018) “A comparison of treatment recommendations by molecular tumor boards worldwide”, JCO Precision Oncology, accepted
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  • Ševa, J., Jähnichen, P., and Leser. U. (2017) “WBI@BioCreative Track 4: Mining protein interactions and mutations for precision medicine”, BioCreative VI Workshop Proceedings.
  • Ševa, J., Kittner, M., Roller, R. and Leser, U. (2017). “Multi-lingual ICD-10 coding using a hybrid rule-based and supervised classification approach at CLEF eHealth 2017”. CLEF Working Papers.
  • Ševa, J., Sänger, M. and Leser, U. (2018). “WBI at CLEF eHealth 2018 Task 1: Language-independent ICD-10 coding using multi-lingual embeddings and recurrent neural networks”, CLEF Experimental IR Meets Multilinguality.
  • Ševa, J., Wackerbauer, M. and Leser, U. (2018). “Identifying Key Sentences for Precision Oncology Using Semi-Supervised Learning”. BioNLP, Melbourne, Australia.
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Funding: BMBF, i:DSem-Program BMBF
Period: 2016 – 2019
Partnering Institutions: Charite Universitätsmedizin Berlin Charite,
Berlin Institute of Health BIH,
Humboldt-Universität zu Berlin HU-Berlin