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

Proteomics01:33

Proteomics

10.2K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
10.2K
MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

7.7K
Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
7.7K
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

2.2K
Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
2.2K
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

8.8K
Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
8.8K

You might also read

Related Articles

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

Sort by
Same author

Mind Your Spectra: Points to be Aware of When Validating the Identification of Isobaric Histone Peptidoforms.

Journal of proteome research·2025
Same author

Plasma ALS and Gal-3BP differentiate early from advanced liver fibrosis in MASLD patients.

Biomarker research·2024
Same author

Fudging the volcano-plot without dredging the data.

Nature communications·2024
Same author

In vitro production of cat-restricted Toxoplasma pre-sexual stages.

Nature·2023
Same author

Controlling for false discoveries subsequently to large scale one-way ANOVA testing in proteomics: Practical considerations.

Proteomics·2023
Same author

<i>In vitro</i> production of cat-restricted <i>Toxoplasma</i> pre-sexual stages by epigenetic reprogramming.

bioRxiv : the preprint server for biology·2023
Same journal

Nanoelectroanalysis with Carbon Nanopipettes Based on Prussian Blue-NiHCF for Hydrogen Peroxide Sensing.

Analytical chemistry·2026
Same journal

Multimodal Detection of α-Amanitin Using a Cell-Free Biosensing Platform.

Analytical chemistry·2026
Same journal

Comment on "Three-Principal-Substrate SERS Profiling Enables Reliable Screening of Serum Biomarkers: A General Approach".

Analytical chemistry·2026
Same journal

Response to "Comment on 'Three-Principal-Substrate SERS Profiling Enables Reliable Screening of Serum Biomarkers: A General Approach' ".

Analytical chemistry·2026
Same journal

In-Depth Characterization of PEGylated Liposomes: Using AF4 and HPLC-CAD in Tandem as a Strategy for Composition Assessment and Postinsertion Optimization.

Analytical chemistry·2026
Same journal

Ferroptosis Revealed by a Bifunctional Nanopipette Sensing the Intracellular Iron-Reactive Oxygen under Dibenzothiophene Exposure.

Analytical chemistry·2026
See all related articles
  1. Home
  3. Performance Is Not All You Need! Comment On "unsupervised Machine Learning For Differential Analysis In Proteomics".
  1. Home
  3. Performance Is Not All You Need! Comment On "unsupervised Machine Learning For Differential Analysis In Proteomics".

Related Experiment Video

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
07:01

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Published on: August 19, 2025

1.3K

Performance Is Not All You Need! Comment on "Unsupervised Machine Learning for Differential Analysis in Proteomics".

Alicia Lionneton1, Christophe Bruley1, Thomas Burger1

  • 1Univ. Grenoble Alpes, CNRS, CEA, INSERM, UA13 BGE, UAR2048 ProFI, EDyP, 38000 Grenoble, France.

Analytical Chemistry
|April 16, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Researchers suggest using unsupervised machine learning for detecting protein differences in proteomics. While promising, they caution against overemphasizing performance and recommend focusing on biological questions alongside these advanced computational tools.

More Related Videos

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions
09:00

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

Published on: April 18, 2025

1.6K
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

2.1K

Related Experiment Videos

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
07:01

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Published on: August 19, 2025

1.3K
A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions
09:00

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

Published on: April 18, 2025

1.6K
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

2.1K

Area of Science:

  • Proteomics
  • Bioinformatics
  • Machine Learning

Background:

  • Traditional statistical methods are commonly used for identifying differentially abundant proteins in proteomic experiments.
  • Recent work by Xu et al. proposes unsupervised machine learning for anomaly detection in this context.
  • The authors acknowledge the potential of these novel computational approaches.

Purpose of the Study:

  • To critically evaluate the benchmarking of unsupervised machine learning methods for differential protein detection in proteomics.
  • To provide complementary guidelines for researchers integrating machine learning into proteomic data analysis.
  • To address concerns regarding the potential overstatement of performance gains and the overshadowing of biological inquiry.

Main Methods:

  • Review and critique of the benchmarking methodology presented in Xu et al.'s article.
  • Analysis of the theoretical underpinnings of performance increments reported for machine learning methods.
  • Consideration of the impact of computational tool adoption on the formulation of biological questions in proteomics.

    • Main Results:

    • The benchmarking approach in the discussed article is deemed restrictive.
    • Reported performance improvements associated with machine learning methods may be overstated.
    • An overemphasis on performance metrics could detract from the primary biological objectives of proteomic studies.

    Conclusions:

    • While unsupervised machine learning shows promise in proteomics, its application requires careful consideration.
    • Researchers should balance the adoption of advanced computational tools with a strong focus on biological relevance.
    • Complementary guidelines are needed to ensure the effective and responsible use of machine learning in proteomic data analysis.