176 citations · 417 across the 7 of their papers we have counts for
8 papers
Estimation of Parameter Distributions for Reaction-Diffusion Equations with Competition using Aggregate Spatiotemporal Data
Kyle Nguyen, Erica M. Rutter, Kevin Flores
Reaction diffusion equations have been used to model a wide range of biological phenomenon related to population spread and proliferation from ecology to cancer. It is commonly ass…
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…
Learning differential equation models from stochastic agent-based model simulations
John T. Nardini, Ruth E. Baker, Matthew J. Simpson +1
Agent-based models provide a flexible framework that is frequently used for modelling many biological systems, including cell migration, molecular dynamics, ecology, and epidemiolo…
Region Growing with Convolutional Neural Networks for Biomedical Image Segmentation
John Lagergren, Erica Rutter, Kevin Flores
In this paper we present a methodology that uses convolutional neural networks (CNNs) for segmentation by iteratively growing predicted mask regions in each coordinate direction. T…
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…
Learning partial differential equations for biological transport models from noisy spatiotemporal data
John Lagergren, John T. Nardini, G. Michael Lavigne +2
We investigate methods for learning partial differential equation (PDE) models from spatiotemporal data under biologically realistic levels and forms of noise. Recent progress in l…
Hybrid modeling and prediction of dynamical systems
Franz Hamilton, Alun Lloyd, Kevin Flores
Scientific analysis often relies on the ability to make accurate predictions of a system's dynamics. Mechanistic models, parameterized by a number of unknown parameters, are often…