Updated: January 2nd, 2024 09:03

2020 review

A tutorial review of mathematical techniques for quantifying tumor heterogeneity

[Review of ]. *MATHEMATICAL BIOSCIENCES AND ENGINEERING*, *17*(4), 3660–3709.

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

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)

UN Sustainable Development Goal Categories

3. Good Health and Well-being
(Web of Science)

Source: Web Of Science

Added: August 3, 2020

2020 journal article

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

*PLOS COMPUTATIONAL BIOLOGY*, *16*(12).

MeSH headings : Computer Simulation; Machine Learning; Neural Networks, Computer; Nonlinear Dynamics

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)

UN Sustainable Development Goal Categories

3. Good Health and Well-being
(Web of Science)

Source: Web Of Science

Added: January 4, 2021

2020 journal article

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

*JOURNAL OF STATISTICAL THEORY AND PRACTICE*, *15*(1).

author keywords: Radar imaging; Dispersive targets; Classification; Convolutional neural network

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 journal article

Learning Equations from Biological Data with Limited Time Samples

*BULLETIN OF MATHEMATICAL BIOLOGY*, *82*(9).

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

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)

UN Sustainable Development Goal Categories

3. Good Health and Well-being
(Web of Science)

Source: Web Of Science

Added: September 28, 2020

2020 journal article

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

author keywords: numerical differentiation; equation learning; sparse regression; partial differential equations; parameter estimation; biological transport

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

*MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT IV*, Vol. 11073, pp. 686–694.

UN Sustainable Development Goal Categories

3. Good Health and Well-being
(Web of Science)

Source: Web Of Science

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.

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

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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