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dc.contributor.authorXiao, Yunchen
dc.contributor.authorThomas, Len
dc.contributor.authorChaplain, Mark Andrew Joseph
dc.identifier.citationXiao , Y , Thomas , L & Chaplain , M A J 2021 , ' Calibrating models of cancer invasion : parameter estimation using approximate Bayesian computation and gradient matching ' , Royal Society Open Science , vol. 8 , no. 6 , 202237 .
dc.identifier.otherPURE: 274559124
dc.identifier.otherPURE UUID: ad6cee3e-4e6a-478b-a77b-591e2208b806
dc.identifier.otherORCID: /0000-0002-7436-067X/work/96489382
dc.identifier.otherORCID: /0000-0001-5727-2160/work/96489554
dc.identifier.otherWOS: 000663721400001
dc.identifier.otherScopus: 85111127910
dc.descriptionFunding: Y.X. is funded by a Doctoral Training Partnership grant from the Engineering and Physical Sciences ResearchCouncil (EPSRC) and a University of St Andrews St Leonard’s International Fee Scholarship.en
dc.description.abstractWe present two different methods to estimate parameters within a partial differential equation model of cancer invasion. The model describes the spatio-temporal evolution of three variables—tumour cell density, extracellular matrix density and matrix degrading enzyme concentration—in a one-dimensional tissue domain. The first method is a likelihood-free approach associated with approximate Bayesian computation; the second is a two-stage gradient matching method based on smoothing the data with a generalized additive model (GAM) and matching gradients from the GAM to those from the model. Both methods performed well on simulated data. To increase realism, additionally we tested the gradient matching scheme with simulated measurement error and found that the ability to estimate some model parameters deteriorated rapidly as measurement error increased.
dc.relation.ispartofRoyal Society Open Scienceen
dc.rightsCopyright © 2021 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License, which permits unrestricted use, provided the original author and source are credited.en
dc.subjectTumour cellsen
dc.subjectCancer invasionen
dc.subjectApproximate Bayesian computationen
dc.subjectBhattacharyya distanceen
dc.subjectGradient matchingen
dc.subjectGeneralized additive modelsen
dc.subjectQA Mathematicsen
dc.subjectQH301 Biologyen
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectSDG 3 - Good Health and Well-beingen
dc.titleCalibrating models of cancer invasion : parameter estimation using approximate Bayesian computation and gradient matchingen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. Applied Mathematicsen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Statisticsen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.description.statusPeer revieweden

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