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

Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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 't,' or...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.

You might also read

Related Articles

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

Sort by
Same author

Increased sodium intake and decreased sodium excretion in ICU-acquired hypernatremia: A prospective cohort study.

Journal of critical care·2021
Same author

Modeling the interference between shear and longitudinal waves under high intensity focused ultrasound propagation in bone.

Physics in medicine and biology·2018
Same author

Differential metabolic effects of oral butyrate treatment in lean versus metabolic syndrome subjects.

Clinical and translational gastroenterology·2018
Same author

Visible Blue Light Therapy: Molecular Mechanisms and Therapeutic Opportunities.

Current medicinal chemistry·2017
Same author

Coarse-grained simulations of poly(propylene imine) dendrimers in solution.

The Journal of chemical physics·2016
Same author

Altered Energetics of Exercise Explain Risk of Rhabdomyolysis in Very Long-Chain Acyl-CoA Dehydrogenase Deficiency.

PloS one·2016
Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·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
See all related articles

Related Experiment Videos

An integrated strategy for prediction uncertainty analysis.

J Vanlier1, C A Tiemann, P A J Hilbers

  • 1Department of BioMedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands. j.vanlier@tue.nl

Bioinformatics (Oxford, England)
|February 23, 2012
PubMed
Summary
This summary is machine-generated.

Mathematical modeling in systems biology estimates unknown parameters from data. This study introduces a new method to analyze prediction uncertainty, improving model reliability despite parameter challenges.

Related Experiment Videos

Area of Science:

  • Systems Biology
  • Computational Biology
  • Biochemical Pathway Modeling

Background:

  • Mathematical modeling is crucial for understanding biochemical pathways.
  • Estimating unknown parameters from limited experimental data leads to significant prediction uncertainty in complex biological models.
  • Addressing prediction uncertainty is vital in Systems Biology.

Purpose of the Study:

  • To develop and demonstrate a robust strategy for analyzing model prediction uncertainty in Systems Biology.
  • To enhance the reliability of model predictions when dealing with parameter uncertainty and non-identifiability.

Main Methods:

  • Integration of profile likelihood analysis with Bayesian estimation.
  • Application of the developed method to a model of the JAK-STAT signaling pathway.

Main Results:

  • The proposed strategy effectively quantifies prediction uncertainty.
  • Identified predictions on unobserved variables with high confidence, even with non-identifiable parameters.
  • Demonstrated the utility of the method in a relevant biological system (JAK-STAT pathway).

Conclusions:

  • The combined profile likelihood and Bayesian estimation approach provides a powerful tool for Systems Biology model prediction uncertainty analysis.
  • This method allows for confident predictions of unobserved variables, enhancing the practical utility of mathematical models in biology.
  • The approach is valuable for interpreting model behavior and identifying reliable predictions in the presence of parameter uncertainty.