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

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

34.6K
VSEPR Theory for Determination of Electron Pair Geometries
34.6K
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

161
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
161
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

622
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...
622
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

107
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...
107
The Aufbau Principle and Hund's Rule03:02

The Aufbau Principle and Hund's Rule

54.3K
To determine the electron configuration for any particular atom, we can build the structures in the order of atomic numbers. Beginning with hydrogen, and continuing across the periods of the periodic table, we add one proton at a time to the nucleus and one electron to the proper subshell until we have described the electron configurations of all the elements. This procedure is called the aufbau principle, from the German word aufbau (“to build up”). Each added electron occupies the...
54.3K
Improving Translational Accuracy02:07

Improving Translational Accuracy

2.7K
2.7K

You might also read

Related Articles

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

Sort by
Same author

Stochastic Control for Bayesian Neural Network Training.

Entropy (Basel, Switzerland)·2022
Same author

Variational Bayesian Inference for Nonlinear Hawkes Process with Gaussian Process Self-Effects.

Entropy (Basel, Switzerland)·2022
Same author

Flexible and Efficient Inference with Particles for the Variational Gaussian Approximation.

Entropy (Basel, Switzerland)·2021
Same author

Exact solution to the random sequential dynamics of a message passing algorithm.

Physical review. E·2021
Same author

A mathematical model of local and global attention in natural scene viewing.

PLoS computational biology·2020
Same author

Interacting Particle Solutions of Fokker-Planck Equations Through Gradient-Log-Density Estimation.

Entropy (Basel, Switzerland)·2020
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

Related Experiment Video

Updated: Aug 9, 2025

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

1.5K

A Score-Based Approach for Training Schrödinger Bridges for Data Modelling.

Ludwig Winkler1, Cesar Ojeda2,3, Manfred Opper2,3,4

  • 1Machine Learning Group, Technische Universität Berlin, 10623 Berlin, Germany.

Entropy (Basel, Switzerland)
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

Schrödinger bridges, a method for generative modeling, were enhanced using a novel score-function approach. This technique efficiently estimates reverse drifts for time-reversed processes, improving generative data modeling and analyzing genetic data evolution.

Keywords:
Schrödinger bridge problemreverse-time stochastic processesscore estimation

More Related Videos

Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures
10:10

Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures

Published on: December 1, 2020

5.0K
RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

31.6K

Related Experiment Videos

Last Updated: Aug 9, 2025

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

1.5K
Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures
10:10

Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures

Published on: December 1, 2020

5.0K
RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

31.6K

Area of Science:

  • Computational statistics
  • Machine learning
  • Bioinformatics

Background:

  • Schrödinger bridges are stochastic processes connecting probability distributions over time.
  • They are increasingly used for generative data modeling.
  • Training these models requires estimating the drift function of a time-reversed process.

Purpose of the Study:

  • To introduce an efficient method for computing reverse drifts in Schrödinger bridges.
  • To apply this method to artificial and real-world genetic data.

Main Methods:

  • A modified score-function-based method was developed.
  • This method is efficiently implemented using a feed-forward neural network.
  • The approach was tested on artificial datasets of varying complexity.

Main Results:

  • The modified score-function method enables efficient computation of reverse drifts.
  • The approach demonstrated effectiveness on artificial datasets.
  • Successful application to genetic data for modeling single-cell RNA measurements was achieved.

Conclusions:

  • The proposed method offers an efficient way to train Schrödinger bridges.
  • This advancement facilitates generative modeling and analysis of time-evolving data, including genetic sequences.