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Related Concept Videos

Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
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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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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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A network-guided penalized regression with application to proteomics data.

Seungjun Ahn1,2, Eun Jeong Oh3,4

  • 1Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States.

Bioinformatics Advances
|March 2, 2026
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Summary
This summary is machine-generated.

This study introduces a novel network-guided regression method to identify prognostic protein biomarkers. The approach effectively identifies hub proteins, advancing biomarker discovery in proteomics and cancer immunotherapy.

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Area of Science:

  • Proteomics
  • Network Theory
  • Biostatistics

Background:

  • Network theory and Gaussian graphical models are used to infer protein interaction networks and identify hub proteins.
  • Limited research exists on the prognostic value of hub proteins in high-dimensional data, especially when adjusting for clinical covariates.

Purpose of the Study:

  • To develop a network-guided penalized regression method for identifying prognostic protein biomarkers.
  • To address the gap in understanding the prognostic role of hub proteins in high-dimensional settings.

Main Methods:

  • Construct a protein interaction network using the Gaussian graphical model to identify hub proteins.
  • Employ adaptive Lasso for variable selection on non-hub proteins, preserving hub proteins and clinical factors.
  • Utilize the network-guided penalized regression approach for prognostic biomarker identification.

Main Results:

  • The proposed network-guided estimators demonstrate variable selection consistency and asymptotic normality.
  • Simulation studies indicate superior performance compared to existing methods.
  • The method was successfully applied to Clinical Proteomic Tumor Analysis Consortium (CPTAC) data.

Conclusions:

  • Identified hub proteins show potential as prognostic biomarkers for various diseases, including rare genetic disorders.
  • The findings suggest promise for advancing biomarker identification in proteomics research.
  • The developed R package (NetGreg) is available on CRAN for public use.