Inverse Reinforcement Learning for Predicting and Understanding Cancer Evolution

Molecular biology information is at the heart of the core biological processes of evolution and adaptation. The evolutionary process drives a number of important disease processes from viral evolution to cancer progression. Approaches such as Bayesian methods, Markov chain modeling, and machine learning (ML) techniques, typically only characterize a small number of either well-defined or arbitrarily-defined stages in the disease process and predict the gross outcomes such as survival or make binary classification (e.g., drug responders vs. non-responders). They are not designed to unravel the complexity of the entire evolutionary process that gradually drives evolutionary progression via individual mutations. In contrast, inverse reinforcement learning (IRL) algorithms closely parallel the step-by-step accumulation of genomic alterations in lineage evolution. IRL is a specific form of machine learning from demonstrations that estimates the reward function of a Markov decision process from examples provided by expert demonstrations. This talk will highlight an example of ongoing work to prototypes an articulate IRL model of the molecular evolution of colorectal cancer.