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

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 proteomics...
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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...

You might also read

Related Articles

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

Sort by
Same author

Harnessing Large-Scale Multi-Omics Data for Risk Prediction and Deep Phenotyping of Valvular Heart Diseases in the General Population.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

A scalable high-throughput serolomics platform for profiling serum antibody responses in large-scale population-based cohorts.

Nature protocols·2026
Same author

Joint association of the frailty index and phenotypic age with all-cause and cause-specific mortality: A prospective cohort study.

Journal of translational internal medicine·2026
Same author

Breastfeeding and risk of maternal type 2 diabetes: a prospective cohort study of 280 000 women in China.

BMJ open·2026
Same author

Ambient Air Pollution and Hospital Admissions of AECOPD in 10 Regions of China: A Self-Controlled Study Based on a Cohort.

Environment & health (Washington, D.C.)·2026
Same author

Dietary Clusters and Mortality Risk in a Chinese Population: The Role of Type 2 Diabetes and Hypertension.

Nutrients·2026

Related Experiment Video

Updated: Jul 15, 2026

Isolation, Characterization, and Proteomic Analysis of Plasma-Derived Extracellular Vesicles for Cardiovascular Biomarker Discovery
05:30

Isolation, Characterization, and Proteomic Analysis of Plasma-Derived Extracellular Vesicles for Cardiovascular Biomarker Discovery

Published on: January 31, 2025

ProLM: a plasma proteomics pretrained model for the general population.

Shizheng Qiu1, Ming Zhao2, Xin Chen3

  • 1Faculty of Computing, Harbin Institute of Technology, Harbin, China.

Nature Communications
|July 13, 2026
PubMed
Summary

A new AI model, ProLM, analyzes plasma proteins to predict 16 chronic diseases, outperforming existing risk scores. This advance offers potential for early disease detection and risk stratification using proteomics.

Related Experiment Videos

Last Updated: Jul 15, 2026

Isolation, Characterization, and Proteomic Analysis of Plasma-Derived Extracellular Vesicles for Cardiovascular Biomarker Discovery
05:30

Isolation, Characterization, and Proteomic Analysis of Plasma-Derived Extracellular Vesicles for Cardiovascular Biomarker Discovery

Published on: January 31, 2025

Area of Science:

  • Biochemistry
  • Computational Biology
  • Proteomics

Background:

  • Plasma proteomics offers insights into human health dynamics.
  • Developing generalizable models for protein expression in population cohorts is challenging.

Purpose of the Study:

  • To develop and evaluate ProLM, a BERT-based model for plasma proteomics.
  • To assess ProLM's ability to predict chronic diseases and stratify risk.

Main Methods:

  • Pretraining ProLM on plasma proteomics data from 15,499 UK Biobank participants.
  • Fine-tuning ProLM for disease-specific prediction.
  • Comparing ProLM-derived risk scores against established models (Age+Sex, ASCVD, PANEL).

Main Results:

  • ProLM captured baseline protein-expression relationships.
  • ProLM risk scores outperformed Age+Sex for all 16 diseases.
  • ProLM outperformed ASCVD for 14 diseases and PANEL for 11 diseases.
  • Identified proteins like GDF15 with expression changes over 15 years before diagnosis.

Conclusions:

  • Pretrained plasma proteomics models like ProLM show promise for early chronic disease risk stratification.
  • Further prospective validation is required for clinical implementation.