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

Regression Toward the Mean01:52

Regression Toward the Mean

6.9K
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...
6.9K
Multiple Regression01:25

Multiple Regression

3.8K
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...
3.8K
Correlation and Regression00:53

Correlation and Regression

3.1K
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...
3.1K
Regression Analysis01:11

Regression Analysis

8.1K
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:
8.1K
Microsoft Excel: Regression Analysis01:18

Microsoft Excel: Regression Analysis

1.5K
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...
1.5K
Molecules and Compounds02:38

Molecules and Compounds

68.2K
Atoms and Molecules
68.2K

You might also read

Related Articles

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

Sort by
Same author

Comprehensive interaction modeling with machine learning improves prediction of disease risk in the UK Biobank.

Nature communications·2025
Same author

Scaling up drug combination surface prediction.

Briefings in bioinformatics·2025
Same author

NMFProfiler: a multi-omics integration method for samples stratified in groups.

Bioinformatics (Oxford, England)·2025
Same author

Chemical reaction enhanced graph learning for molecule representation.

Bioinformatics (Oxford, England)·2024
Same author

Stable biomarker discovery in multi-omics data via canonical correlation analysis.

PloS one·2024
Same author

Attention-based approach to predict drug-target interactions across seven target superfamilies.

Bioinformatics (Oxford, England)·2024
Same journal

Lactate Metabolism Dysregulation Drives the Pathogenesis of Acute Kidney Injury.

Metabolites·2026
Same journal

Librarian: An Open-Access Web Application for High-Resolution Mass Spectral Library Assembly.

Metabolites·2026
Same journal

Purine Metabolism Alterations in Patients with Chronic Heart Failure: A Cross-Sectional Study of Associations with Iron Status, Oxidative Stress, and Anemia.

Metabolites·2026
Same journal

The Gut Microbiome in Heart Failure: Pathways to Inflammation and Therapeutic Targets.

Metabolites·2026
Same journal

Metabolic Mechanisms of Hexavalent Chromium-Induced Splenic Immune Injury via Oxidative Stress and Ferroptosis Pathways in New Zealand Rabbits.

Metabolites·2026
Same journal

Improving Speed and Efficiency of DESI Imaging with the Xevo MRT Mass Spectrometer for Analyte Mapping.

Metabolites·2026
See all related articles

Related Experiment Video

Updated: Jan 21, 2026

Isolation of Specific Genomic Regions and Identification of Associated Molecules by enChIP
09:26

Isolation of Specific Genomic Regions and Identification of Associated Molecules by enChIP

Published on: January 20, 2016

10.9K

Improved Small Molecule Identification through Learning Combinations of Kernel Regression Models.

Céline Brouard1, Antoine Bassé2, Florence d'Alché-Buc2

  • 1Unité de Mathématiques et Informatique Appliquées de Toulouse, UR 875, INRA, 31326 Castanet Tolosan, France. celine.brouard@inra.fr.

Metabolites
|August 4, 2019
PubMed
Summary
This summary is machine-generated.

Input-output kernel regression (IOKR) advances small molecule identification from tandem mass (MS/MS) spectra. New IOKRreverse and IOKRfusion models improve accuracy in identifying molecules from their spectral data.

Keywords:
kernel methodsmachine learningmetabolite identificationstructured prediction

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.5K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.8K

Related Experiment Videos

Last Updated: Jan 21, 2026

Isolation of Specific Genomic Regions and Identification of Associated Molecules by enChIP
09:26

Isolation of Specific Genomic Regions and Identification of Associated Molecules by enChIP

Published on: January 20, 2016

10.9K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.5K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.8K

Area of Science:

  • Computational chemistry
  • Spectroscopy
  • Machine learning

Background:

  • Input-output kernel regression (IOKR) is a state-of-the-art method for small molecule identification using tandem mass (MS/MS) spectra.
  • IOKR predicts molecular fingerprints from spectra, enabling identification by finding molecules with similar fingerprints.

Purpose of the Study:

  • To enhance the Input-output kernel regression (IOKR) framework for improved small molecule identification.
  • To introduce novel IOKRreverse and IOKRfusion models for more accurate spectral analysis.

Main Methods:

  • Formulation of the IOKRreverse model, which maps molecular structures to the MS/MS feature space.
  • Development of IOKRfusion, a method combining multiple IOKR and IOKRreverse models using structured Hinge loss minimization.
  • Optimization via mini-batch stochastic subgradient optimization.

Main Results:

  • Consistent improvements in top-k accuracy were observed for both positive and negative ionization mode data.
  • The new models demonstrated enhanced performance in small molecule identification from MS/MS spectra.

Conclusions:

  • The enhanced IOKR framework, including IOKRreverse and IOKRfusion, offers significant improvements in small molecule identification accuracy.
  • These advancements provide more robust tools for analyzing MS/MS spectral data in chemical identification.