A tutorial review of mathematical techniques for quantifying tumor heterogeneity

A tutorial review of mathematical techniques for quantifying tumor heterogeneity

Intra-tumor and inter-patient heterogeneity are two challenges in developing mathematical models for precision medicine diagnostics. Here we review several techniques that can be used to aid the mathematical modeller in inferring and quantifying both sources of heterogeneity from patient data. These...

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Bibliographic Details
Main Authors: Rebecca Everett, Kevin B. Flores, Nick Henscheid, John Lagergren, Kamila Larripa, Ding Li, John T. Nardini, Phuong T. T. Nguyen, E. Bruce Pitman, Erica M. Rutter
Format: Article
Language:English
Published: AIMS Press 2020-05-01
Series:Mathematical Biosciences and Engineering
Subjects:
cancer heterogeneity
mathematical oncology
tumor growth
glioblastoma multiforme
virtual populations
nonlinear mixed effects
spatiotemporal data
bayesian estimation
generative adversarial networks
non-parametric estimation
variational autoencoders
machine learning
Online Access:https://www.aimspress.com/article/doi/10.3934/mbe.2020207?viewType=HTML

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https://www.aimspress.com/article/doi/10.3934/mbe.2020207?viewType=HTML

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