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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.date.accessioned2023-05-16T20:28:17Z
dc.date.available2023-05-16T20:28:17Z
dc.date.issued2020
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). https://doi.org/10.3934/mbe.2020207en_US
dc.identifier.urihttps://doi.org/10.3934/mbe.2020207
dc.identifier.urihttp://dr.tcnj.edu/handle/2900/4194
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 https://github.com/jtnardin/Tumor-Heterogeneity/ 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.language.isoen_USen_US
dc.publisherAIMS Pressen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCancer heterogeneityen_US
dc.subjectMathematical oncologyen_US
dc.subjectTumor growthen_US
dc.subjectTumors--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
dc.typeArticleen_US
dc.typeTexten_US
prism.publicationNameMathematical Biosciences and Engineeringen_US
prism.volume17
prism.issueIdentifier4
prism.startingPage3660
prism.endingPage3709


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