ArticleJournal of mathematical biology2020
Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer.
Article in Journal of mathematical biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Modeling the spatio-temporal dynamics of air pollution index based on spatial Markov chain model.Environmental monitoring and assessment · 2020Article
- A Cellular Automata Model of Oncolytic Virotherapy in Pancreatic Cancer.Bulletin of mathematical biology · 2020Article
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Authors and funding
2 authors.
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Abstract
A spatial Markov-chain model is formulated for the progression of skin cancer. The model is based on the division of the computational domain into nodal points, that can be in a binary state: either in 'cancer state' or in 'non-cancer state'. The model assigns probabilities for the non-reversible transition from 'non-cancer' state to the 'cancer state' that depend on the states of the neighbouring nodes. The likelihood of transition further depends on the life burden intensity of the UV-rays that the skin is exposed to. The probabilistic nature of the process and the uncertainty in the input data is assessed by the use of Monte Carlo simulations. A good fit between experiments on mice and our model has been obtained.
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