Works (8)

Updated: August 15th, 2023 21:15

2021 review

Learning differential equation models from stochastic agent-based model simulations

[Review of ]. JOURNAL OF THE ROYAL SOCIETY INTERFACE, 18(176).

co-author countries: Australia πŸ‡¦πŸ‡Ί United Kingdom of Great Britain and Northern Ireland πŸ‡¬πŸ‡§ United States of America πŸ‡ΊπŸ‡Έ
author keywords: agent-based models; differential equations; equation learning; population dynamics; disease dynamics
MeSH headings : Learning; Models, Biological; Molecular Dynamics Simulation; Monte Carlo Method; Stochastic Processes
Source: Web Of Science
Added: April 12, 2021

2021 journal article

Topological data analysis distinguishes parameter regimes in the Anderson-Chaplain model of angiogenesis

PLOS COMPUTATIONAL BIOLOGY, 17(6).

co-author countries: United Kingdom of Great Britain and Northern Ireland πŸ‡¬πŸ‡§ United States of America πŸ‡ΊπŸ‡Έ
MeSH headings : Algorithms; Animals; Blood Vessels / anatomy & histology; Blood Vessels / growth & development; Blood Vessels / physiology; Chemotaxis; Computational Biology; Computer Simulation; Humans; Models, Cardiovascular; Neoplasms / blood supply; Neovascularization, Pathologic; Neovascularization, Physiologic; Spatio-Temporal Analysis
Source: Web Of Science
Added: July 19, 2021

2020 review

A tutorial review of mathematical techniques for quantifying tumor heterogeneity

[Review of ]. MATHEMATICAL BIOSCIENCES AND ENGINEERING, 17(4), 3660–3709.

By: R. Everett*, K. Flores*, N. Henscheid, J. Lagergren*, K. Larripa, D. Li, J. Nardini*, P. Nguyen*, E. Pitman, E. Rutter*

author keywords: cancer heterogeneity; mathematical oncology; tumor growth; glioblastoma multiforme; virtual populations; nonlinear mixed effects; spatiotemporal data; Bayesian estimation; generative; adversarial networks; non-parametric estimation; variational autoencoders; machine learning
MeSH headings : Bayes Theorem; Humans; Machine Learning; Models, Theoretical; Neoplasms; Precision Medicine
Source: Web Of Science
Added: August 3, 2020

2020 journal article

Biologically-informed neural networks guide mechanistic modeling from sparse experimental data

PLOS COMPUTATIONAL BIOLOGY, 16(12).

co-author countries: Australia πŸ‡¦πŸ‡Ί United Kingdom of Great Britain and Northern Ireland πŸ‡¬πŸ‡§ United States of America πŸ‡ΊπŸ‡Έ
MeSH headings : Computer Simulation; Machine Learning; Neural Networks, Computer; Nonlinear Dynamics
Source: Web Of Science
Added: January 4, 2021

2020 journal article

Learning Equations from Biological Data with Limited Time Samples

BULLETIN OF MATHEMATICAL BIOLOGY, 82(9).

By: J. Nardini n, J. Lagergren n, A. Hawkins-Daarud *, L. Curtin *, B. Morris*, E. Rutter*, K. Swanson*, K. Flores n

co-author countries: United Kingdom of Great Britain and Northern Ireland πŸ‡¬πŸ‡§ United States of America πŸ‡ΊπŸ‡Έ
author keywords: Equation learning; Numerical differentiation; Sparse regression; Model selection; Partial differential equations; Parameter estimation; Population dynamics; Glioblastoma multiforme
MeSH headings : Computational Biology / methods; Glioblastoma; Humans; Learning; Mathematical Concepts; Models, Biological; Nonlinear Dynamics
Source: Web Of Science
Added: September 28, 2020

2020 journal article

Learning partial differential equations for biological transport models from noisy spatio-temporal data

By: J. Lagergren n, J. Nardini n, G. Michael Lavigne n, E. Rutter n & K. Flores n

co-author countries: United States of America πŸ‡ΊπŸ‡Έ
author keywords: numerical differentiation; equation learning; sparse regression; partial differential equations; parameter estimation; biological transport
Source: Web Of Science
Added: March 30, 2020

2019 journal article

The influence of numerical error on parameter estimation and uncertainty quantification for advective PDE models

INVERSE PROBLEMS, 35(6).

By: J. Nardini* & D. Bortz*

co-author countries: United States of America πŸ‡ΊπŸ‡Έ
author keywords: inverse problems; numerical analysis; uncertainty quantification; autocorrelation
Source: Web Of Science
Added: June 17, 2019

2013 journal article

Quantifying CFSE label decay in flow cytometry data

APPLIED MATHEMATICS LETTERS, 26(5), 571–577.

By: H. Banks n, A. Choi n, T. Huffman n, J. Nardini n, L. Poag n & W. Thompson n

co-author countries: United States of America πŸ‡ΊπŸ‡Έ
author keywords: Carboxyfluorescein succinimidyl ester (CFSE); Ordinary differential equation models; Inverse problems; Exponential decay; Gompertz growth; Akiake Information
Source: Web Of Science
Added: August 6, 2018