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Related Concept Videos

Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Related Experiment Video

Updated: Dec 20, 2025

Low Molecular Weight Protein Enrichment on Mesoporous Silica Thin Films for Biomarker Discovery
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Protein-protein correlations based variable dimension expansion algorithm for high efficient serum biomarker

Juanjuan Xie1, Lei Zhang1, Zhangwei Chen2

  • 1Minhang Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, 201199, China.

Analytica Chimica Acta
|May 23, 2020
PubMed
Summary
This summary is machine-generated.

Researchers developed an efficient serum biomarker discovery pipeline. This method accurately identified dilated cardiomyopathy biomarkers, achieving 88.6% classification accuracy using machine learning.

Keywords:
5′-nucleotidaseDilated cardiomyopathyHeart failureMachine learningScheduled multiple reaction monitoring

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Area of Science:

  • Biomedical Science
  • Proteomics
  • Clinical Diagnostics

Background:

  • Serum biomarkers are crucial for non-invasive disease diagnosis.
  • Discovering specific and sensitive biomarkers remains challenging due to protein abundance variations.
  • Existing methods often struggle to identify low-abundance, disease-specific proteins in complex serum matrices.

Purpose of the Study:

  • To develop and validate a novel, high-specificity serum biomarker discovery pipeline.
  • To identify and quantify potential protein biomarkers for dilated cardiomyopathy directly in serum.
  • To enhance disease classification accuracy using machine learning and novel variable generation.

Main Methods:

  • Utilized dysregulated proteins from primary tissue as initial biomarker candidates.
  • Employed scheduled multiple reaction monitoring (SMRM) for precise quantification of candidate proteins in serum.
  • Introduced protein-protein correlation-variable dimensionality extension (PPC-VDE) to improve classification specificity.

Main Results:

  • Successfully applied the pipeline to discover biomarkers for dilated cardiomyopathy.
  • Achieved 88.6% accurate classification distinguishing dilated cardiomyopathy, ischemic cardiomyopathy, and healthy controls.
  • Demonstrated the pipeline's ability to circumvent high-abundance protein interference.

Conclusions:

  • The developed pipeline offers a straightforward and efficient approach for serum biomarker discovery.
  • This method holds significant potential for broad clinical applications in disease diagnostics.
  • The PPC-VDE technique enhances the utility of proteomic data for machine learning-based classification.