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July 23, 2025

A Proteomics-Driven Machine Learning Tool for Distinguishing ET from pre-PMF

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Authors

LZLei ZhangQWQing WenTSTing Sun

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Overview

Retrospective study identifies a proteomic model that improves diagnosis of ET compared to clinical methods, highlighting its potential in MPN detection.

Key Points

  • The study compares diagnostic accuracy between proteomic profiling and clinical variables for essential thrombocythemia.
  • A 9-protein classifier achieved an AUC of 0.895, indicating robust discrimination between ET and pre-PMF.
  • Logistic regression was utilized to assess clinical predictors in a cohort of 440 patients for model development.
  • The proteomic approach shows promise for early-stage diagnosis of myeloproliferative neoplasms, enhancing clinical decision-making.

Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/689a0621e6551bb0af8cdc0bhttps://doi.org/10.21203/rs.3.rs-7128322/v1
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Non-invasive multiparametric characterization of essential thrombocythemia, premyelofibrosis, and overt myelofibrosis2026
  2. 2A lasso and random forest model using flow cytometry data identifies primary myelofibrosis2024 · 1 citations
  3. 3Proteomic analysis identifying biomarkers in the progression from essential thrombocythemia to post-essential thrombocythemia myelofibrosis: A retrospective cohort study2025
  4. 4Multi-omics differences in the bone marrow between essential thrombocythemia and prefibrotic primary myelofibrosis2024 · 1 citations
  5. 5Characterisation of the megakaryocyte proteome in patients with Philadelphia negative myeloproliferative neoplasms2025