Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
100
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

696
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
696
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

247
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
247
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

85
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
85
Compartment Models: Single-Compartment Model01:14

Compartment Models: Single-Compartment Model

2.4K
The single-compartment model serves as a simplified representation of the human body. This model assumes that the body functions as a single, well-mixed open compartment. When a drug is administered intravenously, it enters the body and quickly distributes uniformly. The drug then undergoes biotransformation and elimination, ultimately leaving the body. The volume of this compartment is referred to as the apparent volume of distribution into which the drug can uniformly distribute. In this...
2.4K
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

156
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
156

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Identification of patients receiving amyloid-targeting therapies in observational studies using amyloid PET trajectories: Insights from LEADS.

Alzheimer's & dementia (Amsterdam, Netherlands)·2026
Same author

Development and validation of a harmonized memory score for multicenter Alzheimer's disease and related dementia research.

Alzheimer's research & therapy·2026
Same author

Tau topography subtypes account for clinical heterogeneity and longitudinal trajectories in early-onset Alzheimer's disease.

Brain communications·2026
Same author

Salmonella Typhimurium hijacks a host glucose transporter for intravacuolar proliferation.

Cell communication and signaling : CCS·2026
Same author

Optically detected and radio wave-controlled spin chemistry in flavoproteins.

Nature biotechnology·2026
Same author

Computational learning phenotypes are not related to individual differences in resting-state fMRI connectivity.

Frontiers in neuroscience·2026
Same journal

Simplifying debiased inference via automatic differentiation and probabilistic programming.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Principal stratification with U-statistics under principal ignorability.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Causal K-Means Clustering.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Inference of dependency knowledge graph for Electronic Health Records.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Correction to: Inference of dependency knowledge graph for Electronic Health Records.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Harmonized Estimation of Subgroup-Specific Treatment Effects in Randomized Trials: The Use of External Control Data.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
See all related articles

Related Experiment Video

Updated: Sep 5, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

Principal Manifold Estimation via Model Complexity Selection.

Kun Meng1, Ani Eloyan1

  • 1Department of Biostatistics, Brown University School of Public Health, Providence, RI 02903, USA.

Journal of the Royal Statistical Society. Series B, Statistical Methodology
|July 11, 2022
PubMed
Summary
This summary is machine-generated.

We introduce principal manifolds for modeling complex, high-dimensional data. This method enhances accuracy, handles outliers, and improves computation speed for diverse applications, including medical imaging.

Keywords:
lung cancersplinestotal squared curvaturetumor interior

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.0K
Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.3K

Related Experiment Videos

Last Updated: Sep 5, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.0K
Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.3K

Area of Science:

  • Data modeling
  • Dimensionality reduction
  • Geometric data analysis

Background:

  • High-dimensional data presents challenges for traditional modeling techniques.
  • Existing methods like principal component analysis (PCA) have limitations in capturing complex data structures.
  • Modeling data with arbitrary intrinsic dimensions requires flexible frameworks.

Purpose of the Study:

  • To propose a novel framework of principal manifolds for modeling high-dimensional data.
  • To develop methods for model complexity selection, outlier elimination, and computational efficiency.
  • To introduce techniques for identifying interiors of curved and surface-like data structures.

Main Methods:

  • The framework is based on Sobolev spaces, allowing modeling of data with any intrinsic dimension.
  • A novel method for model complexity selection is proposed to prevent overfitting and improve speed.
  • Techniques are developed for identifying the interiors of circle-like curves and cylinder/ball-like surfaces.
  • The approach incorporates principal component analysis and principal curve algorithms as special cases.

Main Results:

  • The principal manifold framework successfully models high-dimensional data across various intrinsic dimensions.
  • The proposed model complexity selection method enhances robustness against outliers and speeds up computation.
  • The methods for identifying data interiors demonstrate effectiveness on complex geometric shapes.
  • Simulations show the proposed approach outperforms existing methods.

Conclusions:

  • Principal manifolds offer a powerful and flexible framework for high-dimensional data modeling.
  • The developed methods address key challenges in model selection, outlier handling, and computational performance.
  • The approach has practical applications in fields such as medical image analysis, exemplified by lung cancer tumor estimation.