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

You might also read

Related Articles

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

Sort by

Same author

Associations of proteomic age clocks with lifestyle risk factors, incident chronic diseases and mortality in two European cohorts.

Nature aging·2026

Same author

The blood metabolome of brain health in midlife and influences of genes, microbiome and exposome.

Nature aging·2026

Same author

A lipidomic based metabolic age score for monitoring the effects of lifestyle and diet on metabolic disease risk.

Research square·2026

Same author

Exploring sex-specific causal links between thousands of proteins and lipid metabolism using the UK Biobank Pharma Proteomics Project data.

Biology of sex differences·2026

Same author

Prediagnostic serum iodine and selenium in relation to breast cancer survival.

BJS open·2026

Same author

Circulating lipids are related to longitudinal changes of ATN biomarkers for Alzheimer's disease.

Molecular psychiatry·2026

Same journal

Thermo-flux: generation and analysis of thermodynamic-stoichiometric metabolic network models.

Molecular systems biology·2026

Same journal

Paradoxical non-catalytic kinase functions are driven by inhibitor-induced displacement of autoinhibitory domains.

Molecular systems biology·2026

Same journal

E. coli prepares for starvation by dramatically remodeling its proteome in the first hours after loss of nutrients.

Molecular systems biology·2026

Same journal

Common xenobiotics modulate gut microbial responses to low‑calorie sweeteners in vitro.

Molecular systems biology·2026

Same journal

ParTIpy: a scalable framework for archetypal analysis and Pareto task inference.

Molecular systems biology·2026

Same journal

Quantitative interactome mapping of skeletal muscle insulin resistance.

Molecular systems biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 15, 2026

A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation

A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation

Published on: April 24, 2020

Machine learning-guided deconvolution of plasma protein levels.

Maik Pietzner^1,2,3, Carl Beuchel^4,5, Kamil Demircan^4,6

¹Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany. maik.pietzner@bih-charite.de.

Molecular Systems Biology

|October 9, 2025

Summary

This summary is machine-generated.

Machine learning identified key factors influencing blood protein levels in over 43,000 individuals. Modifiable factors explained more protein variance than genetics, aiding biomarker discovery.

Keywords:

Biomarker Drugs Enrichment Plasma Proteomics

More Related Videos

Protein Digestion, Ultrafiltration, and Size Exclusion Chromatography to Optimize the Isolation of Exosomes from Human Blood Plasma and Serum

Protein Digestion, Ultrafiltration, and Size Exclusion Chromatography to Optimize the Isolation of Exosomes from Human Blood Plasma and Serum

Published on: April 13, 2018

Peptide and Protein Quantification Using Automated Immuno-MALDI iMALDI

Peptide and Protein Quantification Using Automated Immuno-MALDI iMALDI

Published on: August 18, 2017

Related Experiment Videos

Last Updated: Jan 15, 2026

A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation

A Plasma Sample Preparation for Mass Spectrometry using an Automated Workstation

Published on: April 24, 2020

Protein Digestion, Ultrafiltration, and Size Exclusion Chromatography to Optimize the Isolation of Exosomes from Human Blood Plasma and Serum

Protein Digestion, Ultrafiltration, and Size Exclusion Chromatography to Optimize the Isolation of Exosomes from Human Blood Plasma and Serum

Published on: April 13, 2018

Peptide and Protein Quantification Using Automated Immuno-MALDI iMALDI

Peptide and Protein Quantification Using Automated Immuno-MALDI iMALDI

Published on: August 18, 2017

Area of Science:

Proteomics
Biomarker Discovery
Machine Learning

Background:

Proteomic techniques measure thousands of blood proteins, but understanding their origins is crucial for biomarker development.
Limited knowledge of factors influencing plasma protein levels hinders clinical translation.

Purpose of the Study:

To systematically identify participant and sample characteristics that explain variance in plasma protein levels using machine learning.
To develop a framework for identifying potential drug target engagement markers and disease-specific biomarkers.

Main Methods:

Applied machine learning to analyze plasma protein levels and >1800 participant/sample characteristics in 43,240 individuals.
Clustered proteins based on shared explanatory factors and integrated findings with genetic and drug data into a knowledge graph.

Main Results:

A median of 20 factors explained 19.4% of protein variance, with modifiable factors (10.0%) contributing more than genetic variation (3.9%).
Explanatory factors were largely consistent across sexes and ancestral groups.
Identified matrix metalloproteinase 12 as a potential biomarker for abdominal aortic aneurysm.

Conclusions:

Machine learning can systematically uncover factors driving plasma protein levels, advancing biomarker discovery.
The developed framework and knowledge graph facilitate the identification of protein biomarkers and potential drug targets.
This resource enables phenotype enrichment and provides tools for exploring proteomic data and findings.