| Biologically-informed neural networks guide mechanistic modeling from sparse experimental data JH Lagergren, JT Nardini, RE Baker, MJ Simpson, KB Flores PLoS computational biology 16 (12), e1008462, 2020 | 86 | 2020 |
| Learning partial differential equations for biological transport models from noisy spatio-temporal data JH Lagergren, JT Nardini, G Michael Lavigne, EM Rutter, KB Flores Proceedings of the Royal Society A 476 (2234), 20190800, 2020 | 75 | 2020 |
| Modeling keratinocyte wound healing dynamics: cell–cell adhesion promotes sustained collective migration JT Nardini, DA Chapnick, X Liu, DM Bortz Journal of theoretical biology 400, 103-117, 2016 | 60 | 2016 |
| Learning differential equation models from stochastic agent-based model simulations JT Nardini, RE Baker, MJ Simpson, KB Flores Journal of the Royal Society Interface 18 (176), 20200987, 2021 | 49 | 2021 |
| Analyzing collective motion with machine learning and topology D Bhaskar, A Manhart, J Milzman, JT Nardini, KM Storey, CM Topaz, ... Chaos: An Interdisciplinary Journal of Nonlinear Science 29 (12), 2019 | 46 | 2019 |
| Learning equations from biological data with limited time samples JT Nardini, JH Lagergren, A Hawkins-Daarud, L Curtin, B Morris, ... Bulletin of mathematical biology 82, 1-33, 2020 | 25 | 2020 |
| Topological data analysis distinguishes parameter regimes in the Anderson-Chaplain model of angiogenesis JT Nardini, BJ Stolz, KB Flores, HA Harrington, HM Byrne PLOS Computational Biology 17 (6), e1009094, 2021 | 24 | 2021 |
| Quantifying CFSE label decay in flow cytometry data HT Banks, A Choi, T Huffman, J Nardini, L Poag, WC Thompson Applied mathematics letters 26 (5), 571-577, 2013 | 19 | 2013 |
| Investigation of a Structured Fisher's Equation with Applications in Biochemistry JT Nardini, DM Bortz SIAM journal on applied mathematics 78 (3), 1712-1736, 2018 | 12 | 2018 |
| A tutorial review of mathematical techniques for quantifying tumor heterogeneity R Everett, K B Flores, N Henscheid, J Lagergren, K Larripa, D Li, ... Mathematical Biosciences and Engineering 17 (4), 2020 | 7 | 2020 |
| The influence of numerical error on parameter estimation and uncertainty quantification for advective PDE models JT Nardini, DM Bortz Inverse problems 35 (6), 065003, 2019 | 5 | 2019 |
| Correlation of parameter estimators for models admitting multiple parameterizations K Adoteye, R Baraldi, K Flores, J Nardini, HT Banks, WC Thompson International Journal of Pure and Applied Mathematics 105 (3), 497-522, 2015 | 4 | 2015 |
| Statistical and topological summaries aid disease detection for segmented retinal vascular images JT Nardini, CWJ Pugh, HM Byrne Microcirculation 30 (4), e12799, 2023 | 3 | 2023 |
| The effects of MAPK activity on cell-cell adhesion during wound healing JT Nardini, DA Chapnick, X Liu, D Bortz arXiv preprint arXiv:1506.07081, 2015 | 2 | 2015 |
| Modeling CFSE label decay in flow cytometry data A Choi, T Huffman, J Nardini, L Poag, WC Thompson, HT Banks North Carolina State University. Center for Research in Scientific Computation, 2012 | 2 | 2012 |
| Quantifying collective motion patterns in mesenchymal cell populations using topological data analysis and agent-based modeling KC Nguyen, CD Jameson, SA Baldwin, JT Nardini, RC Smith, JM Haugh, ... Mathematical Biosciences, 109158, 2024 | | 2024 |
| Mathematical Modeling of Multicellular Tumor Spheroids Quantifies Inter-Patient and Inter-Tumor Heterogeneity A Malik, K Nguyen, J Nardini, K Flores, C Krona, S Nelander | | 2024 |
| Forecasting and predicting stochastic agent-based models of cell migration with biologically-informed neural networks JT Nardini arXiv preprint arXiv:2311.04709, 2023 | | 2023 |
| The Influence of Numerical Error on an Inverse Problem Methodology in PDE Models JT Nardini, DM Bortz arXiv preprint arXiv:1807.09652, 2018 | | 2018 |
| Partial Differential Equation Models of Collective Migration During Wound Healing J Nardini University of Colorado at Boulder, 2018 | | 2018 |
Kevin FloresNorth Carolina State UniversityVerified email at ncsu.edu
