Cancer Evolution: Mathematical Models and Computational Inference
By Niko Beerenwinkel, Roland F. Schwarz, Moritz Gerstung, Florian Markowetz. 2014
351 citations recorded by the source corpus.
Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.
Subfields: Cancer Research, Genetics, Modeling and Simulation
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Citations in the corpus
- Reinforcement Learning in Healthcare: A Survey (2019, background): “ard to obtain efficient drug administration (see [85], [101] and references therein). Being a sequential evolutionary process by nature, cancer treatment is a major objective of RL in DTR applications [102], [103]. Table III summaries the major studies of applying RL in various aspects of cancer trea”