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

Plotting of Topographic Maps01:29

Plotting of Topographic Maps

528
Topographic maps represent the Earth's surface features using contour lines, which connect points of equal elevation to create a two-dimensional representation of three-dimensional terrain. Creating a topographic map requires a systematic approach.Begin by plotting a scaled grid and marking intersections corresponding to the survey's elevation data points. Assign elevation values at these intersections to build the base map. Next, determine contour levels using a consistent contour interval,...
528
Regression Analysis01:11

Regression Analysis

8.4K
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:
8.4K
Regression Toward the Mean01:52

Regression Toward the Mean

7.0K
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...
7.0K
Microsoft Excel: Regression Analysis01:18

Microsoft Excel: Regression Analysis

1.6K
Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
1.6K
Multiple Regression01:25

Multiple Regression

4.0K
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...
4.0K
Correlation and Regression00:53

Correlation and Regression

3.4K
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...
3.4K

You might also read

Related Articles

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

Sort by
Same author

Machine learning-based prediction of polyvinyl alcohol product viscosity and design of optimal process conditions.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry·2026
Same author

Data-Driven Design of Organic Semiconductors Exhibiting Low Reorganization Energy via Hierarchical Variational Autoencoders, Gaussian Mixture Regression, and Bayesian Optimization.

Journal of chemical information and modeling·2026
Same author

Generation of Molecules Near the Applicability Domain Boundaries of Property Prediction Models.

Journal of chemical information and modeling·2026
Same author

A general framework for extrapolation-aware prediction reliability in forward and inverse analyses of Gaussian mixture regression models.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry·2026
Same author

Robust machine learning and ensemble learning approach to predict variation in experimental data for multiple measurements and anomalies.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry·2026
Same author

Machine Learning Models Predicting Solubility and Polymerizability of Polyimides Considering Multiple Monomers for CO<sub>2</sub> Separation Membranes.

Molecular informatics·2026

Related Experiment Video

Updated: Feb 4, 2026

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.7K

Data Visualization, Regression, Applicability Domains and Inverse Analysis Based on Generative Topographic Mapping.

Hiromasa Kaneko1

  • 1Department of Applied Chemistry, Meiji University 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa, 214-8571, Japan.

Molecular Informatics
|September 28, 2018
PubMed
Summary
This summary is machine-generated.

Two generative topographic mapping (GTM) methods, GTM-multiple linear regression (GTM-MLR) and GTM-regression (GTMR), enhance data visualization, regression, and inverse analysis. These methods effectively determine applicability domains (ADs) for QSAR and QSPR studies.

Keywords:
Applicability DomainsData VisualizationGenerative Topographic MappingInverse AnalysisRegression

More Related Videos

High-Throughput Analysis of Optical Mapping Data Using ElectroMap
07:36

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

10.1K
Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.5K

Related Experiment Videos

Last Updated: Feb 4, 2026

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.7K
High-Throughput Analysis of Optical Mapping Data Using ElectroMap
07:36

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

10.1K
Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.5K

Area of Science:

  • Computational chemistry and cheminformatics.
  • Machine learning and artificial intelligence.
  • Data mining and analysis.

Background:

  • Generative topographic mapping (GTM) is a powerful dimensionality reduction technique.
  • Existing methods may have limitations in handling complex relationships and determining applicability domains.
  • Quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) modeling require robust analytical tools.

Purpose of the Study:

  • To introduce two novel GTM-based methods: GTM-multiple linear regression (GTM-MLR) and GTM-regression (GTMR).
  • To demonstrate the utility of these methods for data visualization, regression analysis, inverse analysis, and applicability domain determination.
  • To provide open-source code for the proposed algorithms.

Main Methods:

  • GTM-MLR: Calculates prior probability distribution of descriptors (X) using GTM and posterior distribution of property (y) using MLR.
  • GTMR: Performs GTM on combined X and y to obtain joint probability distribution, enabling regression and inverse analysis.
  • Utilizes product rule and Bayes' theorem for inverse analysis in GTM-MLR.

Main Results:

  • Simulations on linear and nonlinear datasets confirmed the effectiveness of both methods.
  • Application to QSAR and QSPR datasets validated their performance in regression and inverse analysis.
  • Both GTM-MLR and GTMR successfully enabled data visualization and applicability domain determination.

Conclusions:

  • GTM-MLR and GTMR are effective and versatile methods for various chemometric tasks.
  • These methods offer improved capabilities for understanding structure-activity/property relationships.
  • The availability of code facilitates wider adoption and further research in the field.