Search research articles

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

QMAP: a benchmark for standardized evaluation of antimicrobial peptide MIC and hemolytic activity regression.

Scientific reports·2026

Same author

Turncoat antibodies unmasked in a model of autoimmune demyelination: From biology to therapy.

Proceedings of the National Academy of Sciences of the United States of America·2025

Same author

Comparative genomics of <i>Pseudomonas paraeruginosa</i>.

Journal of bacteriology·2025

Same author

Automated High-Throughput Quantification of Phenyl-γ-valerolactones and Creatinine in Urine by Laser Diode Thermal Desorption.

Journal of agricultural and food chemistry·2023

Same author

Gut metagenome profile of the Nunavik Inuit youth is distinct from industrial and non-industrial counterparts.

Communications biology·2022

Same author

Author Correction: A truncated anti-CRISPR protein prevents spacer acquisition but not interference.

Nature communications·2022

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 14, 2025

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

On Selecting Robust Approaches for Learning Predictive Biomarkers in Metabolomics Data Sets.

Thibaud Godon¹, Pier-Luc Plante^1,2, Jacques Corbeil¹

¹Université Laval, Quebec City, Quebec G1 V 0A6, Canada.

Analytical Chemistry

|June 12, 2025

Summary

This summary is machine-generated.

Metabolomics biomarker discovery is challenging due to high-dimensional data. This study evaluates machine learning methods across 835 datasets, proposing a novel comparative approach for robust biomarker identification.

More Related Videos

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

Related Experiment Videos

Last Updated: Jun 14, 2025

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

Area of Science:

Metabolomics
Systems Biology
Biomarker Discovery

Background:

Metabolomics studies small molecules in biological systems, offering insights into metabolic processes and health outcomes.
Biomarker discovery in metabolomics is hindered by high-dimensional data, with current machine learning approaches relying on potentially limiting prior hypotheses.
Evaluating machine learning utility requires comprehensive assessment across diverse datasets.

Purpose of the Study:

To assess the true usefulness of machine learning methods in metabolomics biomarker discovery.
To establish a benchmark for evaluating future machine learning methods in the field.
To propose a novel, universally applicable approach for guiding metabolomics data analysis.

Main Methods:

Evaluation of machine learning methods on a large collection of 835 metabolomics datasets.
Comparative analysis of univariate and multivariate models.
Demonstration of the proposed approach using diverse, representative datasets.

Main Results:

Machine learning methods show variable utility in metabolomics, highlighting data diversity and biomarker complexity.
The proposed comparative approach offers guidance across various dataset structures.
Established a benchmark for future machine learning method evaluations in metabolomics.

Conclusions:

Metabolomics data exhibit high diversity, complicating biomarker discovery.
A novel approach comparing univariate and multivariate models provides a unified strategy for data analysis.
The findings offer valuable insights for researchers on applying machine learning and guide future biomarker discovery efforts.