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Pietzner, M.* ; Beuchel, C.* ; Demircan, K.* ; Hoffmann Anton, J.* ; Zeng, W.* ; Römisch-Margl, W. ; Yasmeen, S.* ; Uluvar, B.* ; Zoodsma, M.* ; Koprulu, M.* ; Kastenmüller, G. ; Carrasco-Zanini, J.* ; Langenberg, C.*

Machine learning-guided deconvolution of plasma protein levels.

Mol. Syst. Biol., DOI: 10.1038/s44320-025-00158-6 (2025)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Proteomic techniques now measure thousands of proteins circulating in blood at population scale, but successful translation into clinically useful protein biomarkers is hampered by our limited understanding of their origins. Here, we use machine learning to systematically identify a median of 20 factors (range: 1-37) out of >1800 participant and sample charateristics that jointly explained an average of 19.4% (max. 100.0%) of the variance in plasma levels of ~3000 protein targets among 43,240 individuals. Proteins segregated into distinct clusters according to their explanatory factors, with modifiable characteristics explaining more variance compared to genetic variation (median: 10.0% vs 3.9%), and factors being largely consistent across the sexes and ancestral groups. We establish a knowledge graph that integrates our findings with genetic studies and drug characteristics to guide identification of potential drug target engagement markers. We demonstrate the value of our resource by identifying disease-specific biomarkers, like matrix metalloproteinase 12 for abdominal aortic aneurysm, and by developing a widely applicable framework for phenotype enrichment (R package: https://github.com/comp-med/r-prodente ). All results are explorable via an interactive web portal ( https://omicscience.org/apps/prot_foundation ).
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Publication type Article: Journal article
Document type Scientific Article
Keywords Biomarker ; Drugs ; Enrichment ; Plasma Proteomics; Genetics; Health; Associations; Proteomics; Expression; Atlas
Language english
Publication Year 2025
HGF-reported in Year 2025
ISSN (print) / ISBN 1744-4292
e-ISSN 1744-4292
Journal Molecular Systems Biology
Publisher EMBO Press
Publishing Place Campus, 4 Crinan St, London, N1 9xw, England
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503891-001
Grants HORIZON EUROPE European Research Council
Bundesministerium für Bildung und Forschung
Deutsche Forschungsgemeinschaft
DOI 10.1038/s44320-025-00158-6
WOS ID 001591174100001
Scopus ID 105018311813
PubMed ID 41068475
Erfassungsdatum 2025-10-23
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