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

Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Regression Analysis01:11

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Correlation and Regression00:53

Correlation and Regression

In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a negative...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...

You might also read

Related Articles

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

Sort by
Same author

Single-channel EEG captures tDCS-induced changes in neural reactivity: a pilot study in disorders of consciousness patients and healthy controls.

Therapeutic advances in neurological disorders·2026
Same author

Neural dissociation of cognitive effort and physiological arousal: Multimodal single-channel EEG, cortisol, and HRV evidence from an ecologically valid field study.

International journal of psychophysiology : official journal of the International Organization of Psychophysiology·2026
Same author

Evaluating cognitive decline detection in aging populations with single-channel EEG features based on two studies and meta-analysis.

Scientific reports·2025
Same author

Pre-hospital stroke monitoring past, present, and future: a perspective.

Frontiers in neurology·2024
Same author

Evaluation of Parkinson's disease early diagnosis using single-channel EEG features and auditory cognitive assessment.

Frontiers in neurology·2024
Same author

Development of a multiassay algorithm (MAA) to identify recent HIV infection in newly diagnosed individuals in Indonesia.

iScience·2023
Same journal

GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Investigations on Multiple Protein Scaffold Filling.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Asymmetric Drug-Drug Interaction Prediction Based on Generative Adversarial Networks and Knowledge Graph.

Journal of computational biology : a journal of computational molecular cell biology·2026
See all related articles

Related Experiment Video

Updated: May 23, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Using iterative ridge regression to explore associations between conditioned variables.

Nimrod Bar-Yaakov1, Zehava Grossman, Nathan Intrator

  • 1School of Computer Science, Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel. nimrodby@post.tau.ac.il

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|April 4, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for mapping condition-specific associations between random variables, improving upon traditional dependency networks. The approach efficiently identifies medically relevant links, such as those in drug-resistant human immunodeficiency virus (HIV) variants.

More Related Videos

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

Related Experiment Videos

Last Updated: May 23, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

Area of Science:

  • Computational biology
  • Statistical modeling
  • Network analysis

Background:

  • Traditional Bayesian and dependency networks struggle to distinguish condition-specific associations from background associations.
  • This ambiguity increases the search space and complicates the identification of relevant relationships.
  • Existing methods lack the precision to isolate associations influenced by specific conditions.

Purpose of the Study:

  • To develop a novel method for joint probability mapping that isolates condition-related associations.
  • To create a directed graph representing only associations influenced by a condition variable.
  • To enhance the efficiency and accuracy of identifying condition-specific probabilistic relationships.

Main Methods:

  • Modification of the dependency network method using ridge-regression.
  • Development of a computationally efficient and numerically robust algorithm.
  • Graphical representation where nodes are random variables and edges are condition-related associations.

Main Results:

  • The method successfully generates a directed graph containing only condition-related associations.
  • Demonstrated efficiency in identifying associations relevant to drug resistance in human immunodeficiency virus (HIV).
  • Recovered known treatment-resistance mutation links and revealed novel, statistically significant associations.

Conclusions:

  • The novel method effectively discriminates between conditioned and background associations.
  • This approach significantly reduces the search space and improves the discovery of condition-specific relationships.
  • The method holds potential for uncovering biologically plausible associations in complex biological systems, such as HIV drug resistance.