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

Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

8.6K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
8.6K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

8.7K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
8.7K
Confidence Intervals01:21

Confidence Intervals

9.3K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
9.3K
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

927
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...
927
Bioequivalence Data: Statistical Interpretation01:16

Bioequivalence Data: Statistical Interpretation

76
Body:The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...
76
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

333
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
333

You might also read

Related Articles

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

Sort by
Same journal

Popformer: Learning general signatures of positive selection with a self-supervised transformer.

PLoS computational biology·2026
Same journal

Representational capacity shapes both the "whether" and "how" of social learning.

PLoS computational biology·2026
Same journal

Ten quick tips to SNIFF out sustainable and secure scientific software.

PLoS computational biology·2026
Same journal

Early reduction in aversive Pavlovian bias as a mediator of anhedonia improvement during Behavioural Activation in realistic treatment settings.

PLoS computational biology·2026
Same journal

TB-SERS analyzer: Analysis tool for tuberculosis prediction based on Raman spectroscopy with machine learning and convolutional neural network.

PLoS computational biology·2026
Same journal

Application of a uniaxial force by pulling the skin around the mammary gland may affect the orientation of the ducts and the length of the mammary ductal network: Findings from computational modeling and laboratory experiments.

PLoS computational biology·2026

Related Experiment Video

Updated: Nov 24, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.5K

Confidence intervals by constrained optimization-An algorithm and software package for practical identifiability

Ivan Borisov1, Evgeny Metelkin1

  • 1INSYSBIO LLC, Moscow, Russia.

Plos Computational Biology
|December 21, 2020
PubMed
Summary

We developed Confidence Intervals by Constraint Optimization (CICO), a faster method for estimating model parameters in Systems Biology and Quantitative Systems Pharmacology (QSP) modeling. This algorithm improves the accuracy and efficiency of confidence interval estimation from experimental data.

More Related Videos

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.5K
Author Spotlight: An Optimized Automated Method for Investigating Retinoic Acid Receptors in Neuronal Mitochondria
08:33

Author Spotlight: An Optimized Automated Method for Investigating Retinoic Acid Receptors in Neuronal Mitochondria

Published on: July 28, 2023

814

Related Experiment Videos

Last Updated: Nov 24, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.5K
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.5K
Author Spotlight: An Optimized Automated Method for Investigating Retinoic Acid Receptors in Neuronal Mitochondria
08:33

Author Spotlight: An Optimized Automated Method for Investigating Retinoic Acid Receptors in Neuronal Mitochondria

Published on: July 28, 2023

814

Area of Science:

  • Systems Biology
  • Quantitative Systems Pharmacology (QSP) modeling
  • Computational Biology

Background:

  • Practical identifiability is crucial for assessing the predictability of Systems Biology models.
  • Profile likelihood methods are reliable for parameter estimation but often computationally intensive.
  • Accurate and efficient confidence interval estimation is vital for Systems Biology and QSP model development.

Purpose of the Study:

  • To develop a computationally efficient algorithm for estimating confidence intervals of model parameters.
  • To improve the accuracy of parameter confidence intervals in Systems Biology and QSP models.
  • To reduce the computational cost associated with practical identifiability analysis.

Main Methods:

  • Proposed the Confidence Intervals by Constraint Optimization (CICO) algorithm.
  • Algorithm is based on profile likelihood methods.
  • Numerical implementation allows control over accuracy of confidence interval estimates.

Main Results:

  • The CICO algorithm significantly speeds up confidence interval estimation.
  • Reduced computational cost compared to traditional profile likelihood methods.
  • Validated on Systems Biology models, including Taxol treatment and STAT5 Dimerization models.

Conclusions:

  • CICO offers an accurate and computationally efficient solution for parameter confidence interval estimation in Systems Biology and QSP.
  • The algorithm enhances the practical identifiability analysis of complex biological models.
  • Freely available software packages in Julia and Python facilitate the adoption of CICO.