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Related Concept Videos

Protein Networks02:26

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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.
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Updated: Aug 7, 2025

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PROSE: phenotype-specific network signatures from individual proteomic samples.

Bertrand Jern Han Wong1, Weijia Kong1,2,3, Hui Peng1,2

  • 1School of Biological Sciences, Nanyang Technological University, Singapore.

Briefings in Bioinformatics
|March 12, 2023
PubMed
Summary
This summary is machine-generated.

We developed Proteome Support Vector Enrichment (PROSE), a new computational pipeline to improve proteome coverage and interpretability. PROSE accurately predicts missing proteins and aids in identifying key biological features in complex datasets.

Keywords:
candidate prioritizationenrichment scoringintegrated analysismachine learningnetworkproteomicssupport vector machine (SVM)

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Proteomic studies aim to characterize protein composition but face challenges with low coverage and interpretability.
  • Advancements in mass spectrometry and computational tools have not fully resolved these limitations.

Purpose of the Study:

  • To develop a novel computational pipeline, Proteome Support Vector Enrichment (PROSE), for enhanced protein scoring and prioritization.
  • To improve proteome coverage and interpretability by integrating gene co-expression network data.

Main Methods:

  • PROSE is a fast, scalable, and lightweight pipeline that scores proteins using orthogonal gene co-expression network matrices.
  • It accepts simple protein lists as input and generates enrichment scores for all proteins, including those not detected by mass spectrometry.

Main Results:

  • PROSE demonstrated high accuracy in predicting missing proteins, outperforming 7 other candidate prioritization techniques.
  • Protein scores generated by PROSE strongly correlated with corresponding gene expression data.
  • Application to the Cancer Cell Line Encyclopedia and a breast cancer clinical dataset revealed PROSE's ability to capture phenotypic features and identify potential drivers.

Conclusions:

  • PROSE offers a robust solution for enhancing proteome coverage and interpretability.
  • The pipeline effectively aids in the analysis of complex proteomic datasets, including clinical samples.
  • PROSE is available as a user-friendly Python module, facilitating its broader application in biological research.