A tutorial review of mathematical techniques for quantifying tumor heterogeneity.

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第一作者： Rebecca,Everett

第一单位： Department of Mathematics and Statistics, Haverford College, Haverford, PA, USA.

作者： Rebecca,Everett ^[1] ; Kevin B,Flores ^[2] ; Nick,Henscheid ^[3] ; John,Lagergren ^[2] ; Kamila,Larripa ^[4] ; Ding,Li ^[5] ; John T,Nardini ^[2] ; Phuong T T,Nguyen ^[6] ; E Bruce,Pitman ^[7] ; Erica M,Rutter ^[8]

作者单位： Department of Mathematics and Statistics, Haverford College, Haverford, PA, USA. ^[1] Department of Mathematics, North Carolina State University, Raleigh, NC, USA. ^[2] Department of Medical Imaging, University of Arizona, Tucson, AZ, USA. ^[3] Department of Mathematics, Humboldt State University, Arcata, CA, USA. ^[4] Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA. ^[5] Statistical and Applied Mathematical Sciences Institute, Durham, NC, USA. ^[6] Department of Molecular Biomedical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, USA. ^[7] Department of Materials Design and Innovation, University at Buffalo, Buffalo, NY, USA. ^[8] Department of Applied Mathematics, University of California, Merced, Merced, CA, USA. ^[9]

关键词 Bayesian estimation cancer heterogeneity generative adversarial networks glioblastoma multiforme machine learning mathematical oncology non-parametric estimation nonlinear mixed effects spatiotemporal data tumor growth variational autoencoders virtual populations

主题词贝叶斯定理(Bayes Theorem);人类(Humans);模型, 理论(Models, Theoretical);肿瘤(Neoplasms)

DOI 10.3934/mbe.2020207

PMID 32987550

发布时间 2021-06-21

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准确一般字义偏差语意偏离译文作用小提交