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dc.contributor.authorNardini, John T.
dc.contributor.authorLagergren, John H.
dc.contributor.authorHawkins-Daarud, Andrea
dc.contributor.authorCurtin, Lee
dc.contributor.authorMorris, Bethan
dc.contributor.authorRutter, Erica M.
dc.contributor.authorSwanson, Kristin R.
dc.contributor.authorFlores, Kevin B.
dc.date.accessioned2023-05-16T21:11:46Z
dc.date.available2023-05-16T21:11:46Z
dc.date.issued2020-09-09
dc.identifier.citationNardini, J. T., Lagergren, J. H., Hawkins-Daarud, A., Curtin, L., Morris, B., Rutter, E. M., Swanson, K. R., & Flores, K. B. (2020). Learning equations from biological data with limited time samples. Bulletin of Mathematical Biology, 82, Article 119. https://doi.org/10.1007/s11538-020-00794-zen_US
dc.identifier.urihttps://doi.org/10.1007/s11538-020-00794-z
dc.identifier.urihttps://doi.org/10.48550/arXiv.2005.09622
dc.identifier.urihttp://dr.tcnj.edu/handle/2900/4196
dc.descriptionDepartment of Mathematics and Statisticsen_US
dc.description.abstractEquation learning methods present a promising tool to aid scientists in the modeling process for biological data. Previous equation learning studies have demonstrated that these methods can infer models from rich datasets, however, the performance of these methods in the presence of common challenges from biological data has not been thoroughly explored. We present an equation learning methodology comprised of data denoising, equation learning, model selection and post-processing steps that infers a dynamical systems model from noisy spatiotemporal data. The performance of this methodology is thoroughly investigated in the face of several common challenges presented by biological data, namely, sparse data sampling, large noise levels, and heterogeneity between datasets. We find that this methodology can accurately infer the correct underlying equation and predict unobserved system dynamics from a small number of time samples when the data is sampled over a time interval exhibiting both linear and nonlinear dynamics. Our findings suggest that equation learning methods can be used for model discovery and selection in many areas of biology when an informative dataset is used. We focus on glioblastoma multiforme modeling as a case study in this work to highlight how these results are informative for data-driven modeling-based tumor invasion predictions.en_US
dc.description.sponsorshipNational Science Foundation (U.S.)en_US
dc.description.sponsorshipNational Institute on Agingen_US
dc.description.sponsorshipEngineering and Physical Sciences Research Councilen_US
dc.description.sponsorshipNational Institute of Health (U.S.)en_US
dc.description.sponsorshipJames S. McDonnell Foundationen_US
dc.language.isoen_USen_US
dc.publisherSpringeren_US
dc.rightsFile not available for download due to copyright restrictionsen_US
dc.subjectEquation learningen_US
dc.subjectNumerical differentiationen_US
dc.subjectSparse regressionen_US
dc.subjectModel selectionen_US
dc.subjectPartial differential equationsen_US
dc.subjectParameter estimationen_US
dc.subjectPopulation dynamicsen_US
dc.subjectGlioblastoma multiformeen_US
dc.subjectDifferential equations, Partialen_US
dc.titleLearning equations from biological data with limited time samplesen_US
dc.typeArticleen_US
dc.typeTexten_US
prism.publicationNameBulletin of Mathematical Biologyen_US
prism.volume82


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