Overcoming Observation Bias for Cancer Progression Modeling
Rudolf Schill,
Maren Klever,
Andreas Lösch
et al.
Abstract: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 interactions between genetic events from their co-occurrence patterns. However, current CPMs fail to include effects of g… Show more
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