Jove
Visualize
Contact Us

Related Concept Videos

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

438
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...
438
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

359
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...
359
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

1.5K
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...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Artificial Intelligence Algorithm Based on Genetics to Predict Responses to Interferon-Beta Treatment in Multiple Sclerosis Patients.

Bioengineering (Basel, Switzerland)·2026
Same author

A Collaborative Platform for Advancing Automatic Interpretation in ECG Signals.

Diagnostics (Basel, Switzerland)·2024
Same author

A Deep Learning Approach for Predicting Multiple Sclerosis.

Micromachines·2023
Same author

Optimized Random Forest for Solar Radiation Prediction Using Sunshine Hours.

Micromachines·2022
Same author

Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review.

Micromachines·2021
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles
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 Experiment Video

Updated: May 5, 2026

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
06:37

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Published on: June 15, 2022

5.0K

Causal Interpretation of DBSCAN Algorithm: A Dynamic Modeling for Epsilon Estimation.

K Garcia-Sanchez1, J-L Perez-Ramos1, S Ramirez-Rosales1

  • 1Facultad de Informática, Universidad Autónoma de Querétaro, Av. de las Ciencias S/N, Juriquilla, Santiago de Querétaro 76230, Mexico.

Entropy (Basel, Switzerland)
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel dynamical system approach to optimize the neighborhood parameter epsilon for Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The method improves clustering accuracy in complex datasets by modeling epsilon selection through ordinary differential equations.

Keywords:
DBSCANcausal inferenceinformation complexitystructural causal modelsystem dynamicsuncertainty reduction

More Related Videos

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

10.6K
Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation
10:33

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

Published on: September 4, 2017

19.7K

Related Experiment Videos

Last Updated: May 5, 2026

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
06:37

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Published on: June 15, 2022

5.0K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

10.6K
Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation
10:33

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

Published on: September 4, 2017

19.7K

Area of Science:

  • Data Science
  • Machine Learning
  • Computational Statistics

Background:

  • Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a popular unsupervised learning algorithm.
  • Accurate selection of the neighborhood parameter epsilon is crucial for DBSCAN performance.
  • Traditional epsilon estimation methods struggle with high-dimensional and varying-density data.

Purpose of the Study:

  • To develop a robust method for selecting the DBSCAN neighborhood parameter epsilon.
  • To address limitations of traditional epsilon estimation in complex data scenarios.
  • To provide a principled framework for hyperparameter tuning in DBSCAN.

Main Methods:

  • A three-level perspective (algorithmic, modeling, causal) for DBSCAN hyperparameter selection.
  • Approximation of the ordered k-distance signal using a surrogate dynamical system inspired by mass-spring-damper models.
  • Modeling epsilon variation with ordinary differential equations and system identification via L-BFGS-B optimization.
  • Analysis of causal effects using Pearl's do-calculus and computation of Average Causal Effect (ACE).

Main Results:

  • The proposed method effectively models epsilon variation and identifies structurally informative transition regions.
  • Evaluations on synthetic and real-world datasets (Covtype) demonstrate improved epsilon selection.
  • Achieved ACE values of +0.9352, +0.5148, and +0.9246 indicate significant performance gains over geometric baselines.
  • Computational complexity is near O(N log N) under optimal conditions.

Conclusions:

  • The surrogate dynamical estimation framework offers a principled and effective approach to DBSCAN epsilon selection.
  • This method enhances clustering reliability in challenging high-dimensional and multi-density datasets.
  • The causal inference perspective provides a deeper understanding of the hyperparameter selection mechanism.