Cancer is a highly heterogeneous disease, meaning that it can vary significantly between patients and even within different regions of a single tumor. This heterogeneity makes it challenging to predict how the disease will progress or respond to treatment. Probabilistic models help address this complexity by accounting for the inherent uncertainties in cancer biology. They allow researchers to:
- Predict patient outcomes based on genetic, clinical, and environmental factors. - Identify potential biomarkers for early detection. - Optimize treatment plans by simulating different therapeutic scenarios.
