John Lagergren

Works (7)

Updated: October 21st, 2024 08:34

2020 article

A tutorial review of mathematical techniques for quantifying tumor heterogeneity

Everett, R., Flores, K. B., Henscheid, N., Lagergren, J., Larripa, K., Li, D., … Rutter, E. M. (2020, January 1). Mathematical Biosciences & Engineering.

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

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
topics (OpenAlex): Radiomics and Machine Learning in Medical Imaging; Mathematical Biology Tumor Growth; Artificial Intelligence in Healthcare and Education
TL;DR: Several techniques that can be used to aid the mathematical modeller in inferring and quantifying both sources of heterogeneity from patient data are reviewed, including virtual populations, nonlinear mixed effects modeling, non-parametric estimation, Bayesian techniques, and machine learning. (via Semantic Scholar)
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Source: Web Of Science
Added: August 3, 2020

2020 article

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

Lagergren, J. H., Nardini, J. T., Baker, R. E., Simpson, M. J., & Flores, K. B. (2020, December 1). PLoS Computational Biology.

By: J. Lagergren n, J. Nardini n, R. Baker *, M. Simpson * & K. Flores n

MeSH headings : Computer Simulation; Machine Learning; Neural Networks, Computer; Nonlinear Dynamics
topics (OpenAlex): Model Reduction and Neural Networks; Heat shock proteins research
TL;DR: BINNs are trained in a supervised learning framework to approximate in vitro cell biology assay experiments while respecting a generalized form of the governing reaction-diffusion partial differential equation (PDE). (via Semantic Scholar)
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Added: January 4, 2021

2020 article

Deep Learning Approach to the Detection of Scattering Delay in Radar Images

Lagergren, J., Flores, K., Gilman, M., & Tsynkov, S. (2020, November 30). Journal of Statistical Theory and Practice, Vol. 15.

By: J. Lagergren n, K. Flores n, M. Gilman n  & S. Tsynkov n 

author keywords: Radar imaging; Dispersive targets; Classification; Convolutional neural network
topics (OpenAlex): Advanced SAR Imaging Techniques; Synthetic Aperture Radar (SAR) Applications and Techniques; Geophysical Methods and Applications
TL;DR: A convolutional neural network is applied to the problem of discrimination between the instantaneous and delayed targets in synthetic aperture radar images, and a trained neural network demonstrates the discrimination quality commensurate with that of the benchmark maximum likelihood-based classifier. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: January 4, 2021

2020 article

Learning Equations from Biological Data with Limited Time Samples

Nardini, J. T., Lagergren, J. H., Hawkins-Daarud, A., Curtin, L., Morris, B., Rutter, E. M., … Flores, K. B. (2020, September 1). Bulletin of Mathematical Biology.

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

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
topics (OpenAlex): Model Reduction and Neural Networks; Gene Regulatory Network Analysis; Mathematical Biology Tumor Growth
TL;DR: This work presents an equation learning methodology comprised of data denoising, equation learning, model selection and post-processing steps that infers a dynamical systems model from noisy spatiotemporal data and highlights how these results are informative for data-driven modeling-based tumor invasion predictions. (via Semantic Scholar)
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Added: September 28, 2020

2020 article

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

Lagergren, J. H., Nardini, J. T., Lavigne, G. M., Rutter, E. M., & Flores, K. B. (2020, February 1). Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences.

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

author keywords: numerical differentiation; equation learning; sparse regression; partial differential equations; parameter estimation; biological transport
topics (OpenAlex): Model Reduction and Neural Networks; Probabilistic and Robust Engineering Design; Gaussian Processes and Bayesian Inference; Neural Networks and Reservoir Computing; Tensor decomposition and applications
TL;DR: It is shown that the ANN methodology outperforms previous denoising methods, including finite differences and both local and global polynomial regression splines, in the ability to accurately approximate partial derivatives and learn the correct PDE model. (via Semantic Scholar)
Source: Web Of Science
Added: March 30, 2020

2018 article

Automated Object Tracing for Biomedical Image Segmentation Using a Deep Convolutional Neural Network

Rutter, E. M., Lagergren, J. H., & Flores, K. B. (2018, January 1). Lecture Notes in Computer Science.

By: E. Rutter n, J. Lagergren n & K. Flores n

topics (OpenAlex): Cell Image Analysis Techniques; AI in cancer detection; Image Processing Techniques and Applications
TL;DR: A novel methodology that uses CNNs for segmentation by mimicking the human task of tracing object boundaries is presented that is more accurate than CNNs alone and orders of magnitude faster than manual segmentation. (via Semantic Scholar)
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Added: August 12, 2019

2018 journal article

Forecasting and Uncertainty Quantification Using a Hybrid of Mechanistic and Non-mechanistic Models for an Age-Structured Population Model

Bulletin of Mathematical Biology, 80(6), 1578–1595.

By: J. Lagergren n, A. Reeder n, F. Hamilton  n, R. Smith n  & K. Flores n

author keywords: State space reconstruction; Uncertainty quantification; Structured population model; Forecasting
MeSH headings : Animals; Bayes Theorem; Coleoptera / pathogenicity; Coleoptera / physiology; Forecasting / methods; Mathematical Concepts; Models, Biological; Multivariate Analysis; Population Dynamics / statistics & numerical data; Uncertainty
topics (OpenAlex): Animal Ecology and Behavior Studies; Ecosystem dynamics and resilience
TL;DR: An analysis of the results from Bayesian inference for the mechanistic model and hybrid models is performed to suggest reasons why hybrid modeling methodology may enable more accurate forecasts of multivariate systems than traditional approaches. (via Semantic Scholar)
Sources: Web Of Science, Crossref, NC State University Libraries
Added: August 6, 2018

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