Search research articles

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

Related Concept Videos

Proteomics

Proteomics

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...

You might also read

Related Articles

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

Sort by

Same author

Bridging data and discovery: a survey on knowledge graphs in AI for science.

National science review·2026

Same author

Simultaneous In-Depth Single-Cell Proteomic and Metabolomic Analysis.

Analytical chemistry·2026

Same author

Response to "Comment on 'Unsupervised Machine Learning for Differential Analysis in Proteomics' ".

Analytical chemistry·2026

Same author

Minimally Invasive Single-Cell Metabolomics Analysis to Unveil Different Trajectories of Individual Cells in Ferroptosis.

Analytical chemistry·2026

Same author

Machine Learning-Driven Extracellular Vesicles Peptidomics Powers Precision Classification of Endometrial Cancer.

Analytical chemistry·2025

Same author

Benchmarking informatics workflows for data-independent acquisition single-cell proteomics.

Nature communications·2025

Same journal

Unveiling the post translational modification code in Alzheimer's pathogenesis: crosstalk between phosphorylation and acetylation.

Expert review of proteomics·2026

Same journal

Plasma proteome profiles of standard-risk and high-risk childhood acute lymphoblastic leukemia.

Expert review of proteomics·2026

Same journal

Potential proteomic biomarkers for monitoring clinical studies in Duchenne/Becker muscular dystrophy.

Expert review of proteomics·2026

Same journal

Proteomics-based approaches to neutrophil biology.

Expert review of proteomics·2026

Same journal

Deciphering mitochondrial metabolic vulnerabilities in ovarian clear cell carcinoma with mass spectrometry-based clinical proteomics.

Expert review of proteomics·2026

Same journal

The proteomic origin of the genetic code.

Expert review of proteomics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 9, 2025

Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry

Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry

Published on: April 4, 2018

Deep learning approaches for data-independent acquisition proteomics.

Yi Yang¹, Ling Lin¹, Liang Qiao¹

¹Department of Chemistry, Shanghai Stomatological Hospital, and Minhang Hospital, Fudan University, Shanghai China.

Expert Review of Proteomics

|December 17, 2021

Summary

This summary is machine-generated.

Deep learning is revolutionizing data-independent acquisition (DIA) proteomics. These advanced methods enhance spectral library prediction and feature scoring for more comprehensive proteomic analysis.

Keywords:

Data-independent acquisition de novo sequencing deep learning detectability fragment spectrum ion mobility post-translational modifications retention time spectral library statistical control

More Related Videos

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

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

Published on: August 19, 2025

Proteomic Profile of EPS-Urine through FASP Digestion and Data-Independent Analysis

Published on: May 8, 2021

Related Experiment Videos

Last Updated: Oct 9, 2025

Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry

Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry

Published on: April 4, 2018

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

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

Published on: August 19, 2025

Proteomic Profile of EPS-Urine through FASP Digestion and Data-Independent Analysis

Published on: May 8, 2021

Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Data-independent acquisition (DIA) is a powerful technique for large-scale proteomic studies.
Deep learning methods are increasingly integrated into DIA data analysis pipelines.

Purpose of the Study:

To review and summarize deep learning approaches applied to DIA data analysis.
To highlight key areas of application and future directions in the field.

Main Methods:

Literature search of PubMed, Scopus, and Web of Science for articles and preprints up to December 2021.
Overview of deep learning applications including spectral library prediction, feature scoring, statistical control, and de novo peptide sequencing.

Main Results:

Deep learning significantly advances spectral library prediction, overcoming limitations of experimental libraries.
Challenges remain in managing the statistical burden of large predicted libraries.
Emerging applications include the analysis of post-translational modifications.

Conclusions:

Deep learning is a transformative technology for DIA proteomics, offering enhanced analytical capabilities.
Future research should focus on addressing statistical challenges and expanding applications like PTM analysis.