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: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

178
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...
178
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

89
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...
89
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

134
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
134
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

107
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...
107
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

133
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
133
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

270
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,...
270

You might also read

Related Articles

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

Sort by
Same author

Fibroblast- and Macrophage-Derived Thrombospondin-1 Orchestrates the Fibroinflammatory Niche in Metabolic Dysfunction-Associated Steatohepatitis-Induced Fibrosis.

Diabetes & metabolism journal·2026
Same author

Label-Free Quantification of Bilirubin Using a Refractive Index-Insensitive Nanolaminate SERS Substrate.

Biosensors·2026
Same author

Corrigendum to "Molecular role of developmentally regulated GTP-binding protein 1 in coordinating osteoclast and osteoblast differentiation during bone remodeling" [Mol. Cells 49 (2026) 100342].

Molecules and cells·2026
Same author

Development of predictive models for the prognosis of triple-negative breast cancer using multiple transcriptomic analyses.

PloS one·2026
Same author

Molecular role of developmentally regulated GTP-binding protein 1 in coordinating osteoclast and osteoblast differentiation during bone remodeling.

Molecules and cells·2026
Same author

Flexible Bayesian Inference for Identifying Significantly Correlated Multiple Pathway Sets.

Statistics in medicine·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026
Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Sep 26, 2025

A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

17.9K

Kernel-based hierarchical structural component models for pathway analysis.

Suhyun Hwangbo1,2, Sungyoung Lee2, Seungyeoun Lee3

  • 1Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-747, Korea.

Bioinformatics (Oxford, England)
|April 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces HisCoM-Kernel, a novel pathway analysis method that accounts for complex, non-linear relationships between biomarkers and phenotypes. HisCoM-Kernel demonstrates superior statistical power and identifies more biologically meaningful pathways compared to existing methods.

More Related Videos

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

3.4K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.3K

Related Experiment Videos

Last Updated: Sep 26, 2025

A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

17.9K
A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

3.4K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.3K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • Pathway analysis is crucial for interpreting omics data but often overlooks pathway correlations and assumes linear biomarker-phenotype associations.
  • Existing methods may yield misleading results due to ignoring pathway overlap and complex biological relationships.

Purpose of the Study:

  • To develop a novel pathway analysis approach that models complex, non-linear biomarker-phenotype associations.
  • To simultaneously analyze entire biological pathways while considering the hierarchical structure of biomarkers.

Main Methods:

  • Proposed Hierarchical structural CoMponent analysis using Kernel (HisCoM-Kernel), extending kernel machine regression.
  • Incorporated biomarker-pathway hierarchical structure for simultaneous pathway analysis.
  • Applied the method to diverse omics datasets (RNA-seq, etc.).

Main Results:

  • HisCoM-Kernel demonstrated higher statistical power in simulation studies compared to existing pathway-based methods.
  • The method successfully identified biologically meaningful pathways across three different omics datasets.
  • Showcased superior performance in identifying relevant pathways, including those previously reported.

Conclusions:

  • HisCoM-Kernel offers a flexible and powerful approach for pathway analysis in various omics data.
  • The method effectively models complex biological interactions, improving the interpretability of omics studies.
  • HisCoM-Kernel enhances the discovery of biologically relevant pathways by addressing limitations of current approaches.