A tutorial review of mathematical techniques for quantifying tumor heterogeneity
type: Item
instance of: scholarly article
title: A tutorial review of mathematical techniques for quantifying tumor heterogeneity
zbMATH Open document ID: 1467.92056
author: R. A. Everett
author: K. B. Flores
author: Nick Henscheid
author: John Lagergren
author: Kamila Larripa
author: Ding Li
author: John T. Nardini
author: P.-T. T. Nguyen
author: E. B. Pitman
author: Erica M. Rutter
published in: Mathematical Biosciences and Engineering
publication date: 2021-07-07T00:00:00Z
Mathematics Subject Classification ID: 92C32
Mathematics Subject Classification ID: 92-02
Mathematics Subject Classification ID: 92-01
Mathematics Subject Classification ID: 92-08
zbMATH DE Number: 7369189
zbMATH Keywords: cancer heterogeneity
zbMATH Keywords: mathematical oncology
zbMATH Keywords: tumor growth
zbMATH Keywords: glioblastoma multiforme
zbMATH Keywords: virtual populations
zbMATH Keywords: nonlinear mixed effects
zbMATH Keywords: spatiotemporal data
zbMATH Keywords: Bayesian estimation
zbMATH Keywords: generative adversarial networks
zbMATH Keywords: non-parametric estimation
zbMATH Keywords: variational autoencoders
zbMATH Keywords: machine learning
describes a project that uses: pix2pix
describes a project that uses: AlexNet
describes a project that uses: ImageNet
describes a project that uses: CycleGAN
describes a project that uses: GitHub
MaRDI profile type: Publication
Wikidata QID: Q100312457
full work available at URL: mbe.2020207
OpenAlex ID: W3027778715
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DOI: 10.3934/MBE.2020207
description: scientific article; zbMATH DE number 7369189