About us: Research group “Computational Oncology”

We are a young, interdisciplinary group of scientists using computational methods to decipher cancer. Our main tools are Deep Learning and Computational Modeling. We combine these tools with a clinical perspective on cancer genomics, targeted treatment and immunotherapy. Our focus is gastrointestinal cancer, including cancer of the bowel, stomach, liver and pancreas. To learn more about our research, have a look at our featured publications.

Our affiliations

We are based at RWTH University Hospital in Aachen, Germany. In addition, we are affilated with the Department of Medical Oncology, National Center for Tumor Diseases, Heidelberg, Germany and the Department of Pathology and Data Analytics, University of Leeds, Leeds, UK.

Background: Gastrointestinal cancer

Metastatic gastrointestinal (GI) cancers are prevalent and lethal diseases which have seen multiple new treatment approvals in the last ten years. However, only subsets of patients show a clinically meaningful response to any new drug. In this setting, there is a high clinical need to define predictive biomarkers, to find optimal combination regimes and to identify cellular mechanisms which drive response and resistance.

Our funding

We are funded by RWTH Aachen University (“START” program), the Academy of Sciences and Arts of the state of North-Rhine Westphalia, by the German Cancer Aid (Deutsche Krebshilfe, “Max Eder research group”) and by the German Federal Ministry of Health (BMG, “DEEP LIVER”).

Recent Posts

How to develop AI biomarkers for the clinic

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New job opportunity in our group

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Our research featured in the news

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Large international consortium for deep learning-based genotyping of colorectal cancer: MSIDETECT

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New publication: Pan-cancer image-based detection of clinically actionable genetic alterations

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New preprint: Deep learning detects virus presence in cancer histology

New preprint: Spatial structure governs the mode of tumour evolution

Paper published in Nature Medicine: Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

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