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dc.contributor.authorEverett, Rebecca
dc.contributor.authorFlores, Kevin B.
dc.contributor.authorHenscheid, Nick
dc.contributor.authorLagergren, John H.
dc.contributor.authorLarripa, Kamila
dc.contributor.authorLi, Ding
dc.contributor.authorNardini, John T.
dc.contributor.authorNguyen, Phuong T. T.
dc.contributor.authorPitman, E. Bruce
dc.contributor.authorRutter, Erica M.
dc.identifier.citationEverett, R., B Flores, K., Henscheid, N., Lagergren, J., Larripa, K., Li, D., ... & M Rutter, E. (2020). A tutorial review of mathematical techniques for quantifying tumor heterogeneity. Mathematical Biosciences and Engineering, 17(4).
dc.descriptionDepartment of Mathematics and Statisticsen_US
dc.description.abstractIntra-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 techniques include virtual populations, nonlinear mixed effects modeling, non-parametric estimation, Bayesian techniques, and machine learning. We create simulated virtual populations in this study and then apply the four remaining methods to these datasets to highlight the strengths and weak-nesses of each technique. We provide all code used in this review at so that this study may serve as a tutorial for the mathematical modelling community. This review article was a product of a Tumor Heterogeneity Working Group as part of the 2018–2019 Program on Statistical, Mathematical, and Computational Methods for Precision Medicine which took place at the Statistical and Applied Mathematical Sciences Institute.en_US
dc.publisherAIMS Pressen_US
dc.subjectCancer heterogeneityen_US
dc.subjectMathematical oncologyen_US
dc.subjectTumor growthen_US
dc.subjectGlioblastoma multiformeen_US
dc.subjectVirtual populationsen_US
dc.subjectNonlinear mixed effectsen_US
dc.subjectSpatiotemporal dataen_US
dc.subjectBayesian estimationen_US
dc.subjectGenerative adversarial networksen_US
dc.subjectNon-parametric estimationen_US
dc.subjectVariational autoencodersen_US
dc.subjectMachine learningen_US
dc.titleA tutorial review of mathematical techniques for quantifying tumor heterogeneityen_US
prism.publicationNameMathematical Biosciences and Engineeringen_US

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