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: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

108
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...
108
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

771
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...
771
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

705
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
705
Weighted Mean00:57

Weighted Mean

5.5K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
5.5K

You might also read

Related Articles

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

Sort by
Same author

Adaptive Protein Corona Nanoassemblies Couple Cytokine Signaling with Endogenous Antigen Transport for Systemic Cancer Immunity.

Journal of the American Chemical Society·2026
Same author

Endolysosomal-Targeting Nanovaccine Coordinates Antigen-Adjuvant Delivery to Enable Cross-Protective Immunity against Multidrug-Resistant Enterococci.

ACS nano·2026
Same author

Cytosol-Targeting Delivery of Non-Nucleotide STING Agonist Achieves Inhalable Nanoparticle-Based Anthrax Vaccine.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Inhalable Polymeric Nanoparticle Vaccine for Lysosome-Targeting Co-Delivery of Antigen and Adjuvant With Enhanced Immunoprotection.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Synergistic integration of sensing and device-free photocatalytic degradation: All-in-one CuNCs@CS/MOF nanoplatform for 2-mercaptobenzothiazole.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same author

Culture-Free Microfluidics for Ultra-Rapid Antimicrobial Susceptibility Testing with AI in Resource-Limited Settings.

Analytical chemistry·2026

Related Experiment Video

Updated: Sep 27, 2025

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.2K

Adaptive Weighted Neighbors Method for Sensitivity Analysis.

Chenxi Dai1, Kaifa Wang2

  • 1School of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, 400038, China.

Interdisciplinary Sciences, Computational Life Sciences
|April 15, 2022
PubMed
Summary

This study introduces Adaptive Weighted Neighbors (AWN), a new method for reliable sensitivity analysis (SA) in complex biological and biomedical data. AWN effectively handles nonlinear relationships and skewed distributions, improving factor identification.

Keywords:
Adaptive weighted neighborsBioscienceNonlinearitySensitivity analysisSkewed distribution

More Related Videos

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.
10:14

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.

Published on: December 12, 2012

10.7K
Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice
08:51

Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice

Published on: May 10, 2019

11.9K

Related Experiment Videos

Last Updated: Sep 27, 2025

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.2K
Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.
10:14

Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees Apis mellifera L.

Published on: December 12, 2012

10.7K
Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice
08:51

Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice

Published on: May 10, 2019

11.9K

Area of Science:

  • Biomedical Sciences
  • Biology
  • Data Analysis

Background:

  • Identifying key factors from observational data is crucial for understanding complex biological and biomedical phenomena.
  • Practical applications face limitations due to nonlinear input-output relationships and skewed output distributions.

Purpose of the Study:

  • To develop a more reliable sensitivity analysis (SA) method for extreme cases with nonlinear and skewed data.
  • To introduce the Adaptive Weighted Neighbors (AWN) method for improved factor identification.

Main Methods:

  • Proposed the Adaptive Weighted Neighbors (AWN) method, inspired by weighted k-nearest neighbors.
  • AWN utilizes all training samples, weighting nearby samples more heavily.
  • Bootstrap technique and Jansen's method were employed with AWN for SA.

Main Results:

  • Demonstrated the performance and accuracy of AWN on biological, biomedical, simulated, and case study datasets.
  • AWN effectively overcomes limitations of severely nonlinear and skewed data distributions.
  • Achieved reliable sensitivity analysis results in challenging scenarios.

Conclusions:

  • AWN provides a robust approach for sensitivity analysis in complex datasets.
  • The method is expected to be a valuable complementary tool for factor identification in biological and biomedical research.