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Overcoming Observation Bias for Cancer Progression Modeling

URN to cite this document:
Schill, Rudolf ; Klever, Maren ; Lösch, Andreas ; Hu, Y. Linda ; Vocht, Stefan ; Rupp, Kevin ; Grasedyck, Lars ; Spang, Rainer ; Beerenwinkel, Niko
Date of publication of this fulltext: 24 Jan 2024 11:48


Cancers evolve by accumulating genetic alterations, such as mutations and copy number changes. The chronological order of these events is important for understanding the disease, but not directly observable from cross-sectional genomic data. Cancer progression models (CPMs), such as Mutual Hazard Networks (MHNs), reconstruct the progression dynamics of tumors by learning a network of causal ...


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