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Related Concept Videos

Protein Networks02:26

Protein Networks

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,...
Protein Networks02:26

Protein Networks

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,...
Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

A human functional protein interaction network and its application to cancer data analysis.

Guanming Wu1, Xin Feng, Lincoln Stein

  • 1Ontario Institute for Cancer Research, MaRS Centre, South Tower, 101 College Street, Suite 800, Toronto, ON M5G 0A3, Canada. guanmingwu@gmail.com

Genome Biology
|May 21, 2010
PubMed
Summary

Biologists can now analyze large datasets using a new protein functional network. This network reveals common cancer mechanisms across multiple cancer types, aiding disease research.

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Mapping Dysfunctional Protein-Protein Interactions in Disease
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Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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Mapping Dysfunctional Protein-Protein Interactions in Disease
09:39

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Cancer Genomics

Background:

  • Analyzing large biological datasets is challenging.
  • Pathway-based analysis using protein functional networks can provide insights.
  • Developing a robust system for such analysis is crucial.

Purpose of the Study:

  • To construct an extensive protein functional interaction network.
  • To apply this network for analyzing cancer genomics data.
  • To identify common cancer mechanisms through network patterns.

Main Methods:

  • Integrated curated pathways with diverse data sources (PPIs, coexpression, GO, text-mined interactions).
  • Covered approximately 50% of the human proteome.
  • Applied the network to glioblastoma multiforme (GBM) and other cancer datasets.

Main Results:

  • GBM candidate genes formed a significant cluster in the network.
  • Identified two key network modules enriched in oncogenes and tumor suppressors.
  • Observed similar network patterns in breast, colorectal, and pancreatic cancers.

Conclusions:

  • A reliable functional interaction network was built and applied to cancer data analysis.
  • Network patterns suggest shared mechanisms in cancer biology.
  • The system provides a foundation for network/pathway-based disease analysis platforms.