Dataset of tugHall simulations of cell evolution for colorectal cancer

Nagornov, Iurii S.; Nishino, Junichi; Kato, Mamoru

doi:10.1016/j.dib.2021.106719

Cited by 2 publications

(2 citation statements)

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“…The most important part of tests is the model of a cancer cell evolution as an example of branching processes with a high probability of cell population extinction. In the research the tugHall simulator was used from work Nagornov and Kato (2020) as well as a dataset of simulations Nagornov et al (2021). The model has 27 parameters, the 7 parameters of them were fixed because of expert estimation Nagornov and Kato (2020).…”

Section: Cancer Cell Evolutionmentioning

confidence: 99%

“…That's why for the initial uniform distribution of parameters, the branching processes model yields unevenly distributed data that consists of sparse and dense regions. Another problem in the parameter estimation task of branching processes model is the stochastic character of data, especially for cancer cell evolution Nagornov et al (2021). Simulation, based on a model of cell mutations, population evolution and tumor/cancer subpopulations, mainly leads to the emergence of many clones and rarely to the appearance of cancer cells.…”

Section: Introductionmentioning

confidence: 99%

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Approximate Bayesian Computation Based on Maxima Weighted Isolation Kernel Mapping

Nagornov¹

2022

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Motivation:The branching processes model yields unevenly stochastically distributed data that consists of sparse and dense regions. The work tries to solve the problem of an precise evaluation of a parameter for this type of a model. The application of branching processes model to cancer cell evolution has many difficulties like high dimensionality and rare appearance of a result of interest. Moreover we would like to solve the ambitious task of obtaining the coefficients of the model reflecting the relationship of driver genes mutations and cancer hallmarks on the basis of personal data of variant allele frequencies. Results: The Approximate Bayesian computation method based on Isolation kernel is designed. The method includes a transformation row data to a Hilbert space (mapping) and measure the similarity between simulation points and maxima weighted Isolation kernel mapping related to observation point. Also, we designed heuristic algorithm to find parameter estimation without gradient calculation and dimension-independent. The advantage of the proposed machine learning method is shown for multidimensional test data as well as for an example of cancer cell evolution.

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Section: Cancer Cell Evolutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%