Quantifying uncertainty of predictions from cancer progression models.

Abstract excerpt

MOTIVATION: Cancer progresses through the accumulation of genomic events. Cancer progression models such as Mutual Hazard Networks (MHNs) describe this dynamic, enabling prediction of temporal event positions and patient-specific risks of acquiring mutations. However, current MHN analyses rely on single most likely models and do not quantify the uncertainty inherent to parameter estimation. Assessing forecast...

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• Bayes Theorem
• Algorithms
• Computational Biology
• Disease Progression
• Markov Chains