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pro vyhledávání: '"Nardini, John T."'
Autor:
Nardini, John T.
Collective migration is an important component of many biological processes, including wound healing, tumorigenesis, and embryo development. Spatial agent-based models (ABMs) are often used to model collective migration, but it is challenging to thor
Externí odkaz:
http://arxiv.org/abs/2311.04709
Disease complications can alter vascular network morphology and disrupt tissue functioning. Diabetic retinopathy, for example, is a complication of types 1 and 2 diabetes mellitus that can cause blindness. Microvascular diseases are assessed by visua
Externí odkaz:
http://arxiv.org/abs/2202.09708
Autor:
Nguyen, Kyle C., Jameson, Carter D., Baldwin, Scott A., Nardini, John T., Smith, Ralph C., Haugh, Jason M., Flores, Kevin B.
Publikováno v:
In Mathematical Biosciences April 2024 370
Autor:
Nardini, John T., Stolz, Bernadette J., Flores, Kevin B., Harrington, Heather A., Byrne, Helen M.
Angiogenesis is the process by which blood vessels form from pre-existing vessels. It plays a key role in many biological processes, including embryonic development and wound healing, and contributes to many diseases including cancer and rheumatoid a
Externí odkaz:
http://arxiv.org/abs/2101.00523
Publikováno v:
Journal of the Royal Society Interface 18 (176) 2021
Agent-based models provide a flexible framework that is frequently used for modelling many biological systems, including cell migration, molecular dynamics, ecology, and epidemiology. Analysis of the model dynamics can be challenging due to their inh
Externí odkaz:
http://arxiv.org/abs/2011.08255
Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying dynamics of biological systems from sparse experimental data. In the present work, BINNs are train
Externí odkaz:
http://arxiv.org/abs/2005.13073
Autor:
Nardini, John T., Lagergren, John H., Hawkins-Daarud, Andrea, Curtin, Lee, Morris, Bethan, Rutter, Erica M., Swanson, Kristin R., Flores, Kevin B.
Equation 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
Externí odkaz:
http://arxiv.org/abs/2005.09622
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Autor:
Bhaskar, Dhananjay, Manhart, Angelika, Milzman, Jesse, Nardini, John T., Storey, Kathleen, Topaz, Chad M., Ziegelmeier, Lori
Publikováno v:
Chaos 29, 123125 (2019)
We use topological data analysis and machine learning to study a seminal model of collective motion in biology [D'Orsogna et al., Phys. Rev. Lett. 96 (2006)]. This model describes agents interacting nonlinearly via attractive-repulsive social forces
Externí odkaz:
http://arxiv.org/abs/1908.09081
We investigate methods for learning partial differential equation (PDE) models from spatiotemporal data under biologically realistic levels and forms of noise. Recent progress in learning PDEs from data have used sparse regression to select candidate
Externí odkaz:
http://arxiv.org/abs/1902.04733