176 citations · 392 across the 14 of their papers we have counts for
8 papers · 1 filter
Spatial Model Selection and Uncertainty Quantification: Comparing Continuous and Discrete Wound Healing Models
John T. Nardini, Jana L. Gevertz
All data-driven modeling tasks (e.g., parameter estimation, uncertainty quantification, and data forecasting) require the selection of a mathematical model. An overlooked aspect of…
Quantifying topological features and irregularities in zebrafish patterns using the sweeping-plane filtration
Nour Khoudari, John Nardini, Alexandria Volkening
Complex patterns emerge across a wide range of biological systems. While such patterns often exhibit remarkable robustness, variation and irregularity exist at multiple scales and…
SSRCA: a novel machine learning pipeline to perform sensitivity analysis for agent-based models
Edward H. Rohr, John T. Nardini
Agent-based models (ABMs) are widely used in biology to understand how individual actions scale into emergent population behavior. Modelers employ sensitivity analysis (SA) algorit…
Forecasting and predicting stochastic agent-based model data with biologically-informed neural networks
John T. Nardini
Collective migration is an important component of many biological processes, including wound healing, tumorigenesis, and embryo development. Spatial agent-based models (ABMs) are o…
Statistical and Topological Summaries Aid Disease Detection for Segmented Retinal Vascular Images
John T. Nardini, Charles W. J. Pugh, Helen M. Byrne
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…
Topological data analysis distinguishes parameter regimes in the Anderson-Chaplain model of angiogenesis
John T. Nardini, Bernadette J. Stolz, Kevin B. Flores +2
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 heali…
Biologically-informed neural networks guide mechanistic modeling from sparse experimental data
John H. Lagergren, John T. Nardini, Ruth E. Baker +2
Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying dynamics of biological syste…
Learning Equations from Biological Data with Limited Time Samples
John T. Nardini, John H. Lagergren, Andrea Hawkins-Daarud +5
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 me…