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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...
Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

For any given polymer, the weight average molecular weight (Mw) is higher than, if not equal to, the number average molecular weight (Mn). The only situation in which the weight average molecular weight and the number average molecular weight are equal is when a polymer consists only of chains with equal molecular weight. However, this never happens in a synthetic polymer, since it is difficult to control the polymerization process up to a molecular level with accuracy to a hundred percent.
pV-Diagrams01:18

pV-Diagrams

The pV diagram, which is a graph of pressure versus volume of the gas under study, is helpful in describing certain aspects of the substance. When the substance behaves like an ideal gas, the ideal gas equation describes the relationship between its pressure and volume. On a pV diagram, it is common to plot an isotherm, which is a curve showing p as a function of V with the number of molecules and the temperature fixed. Then, for an ideal gas, the product of the pressure of the gas and its...
Pore Size Distribution01:23

Pore Size Distribution

In concrete, the pore size distribution significantly influences the material's properties. Capillary pores, markedly larger than gel pores, form a vast network within partially hydrated cement paste, reducing the concrete's strength and increasing its permeability. This heightened permeability leads to a greater risk of damage from environmental factors like freeze-thaw cycles and chemical attacks, with the extent of vulnerability also being tied to the water-to-cement ratio.
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Related Experiment Video

Updated: Jun 23, 2026

Mapping Dysfunctional Protein-Protein Interactions in Disease
09:39

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

Complex discovery from weighted PPI networks.

Guimei Liu1, Limsoon Wong, Hon Nian Chua

  • 1School of Computing, National University of Singapore, Singapore and Institute for Infocomm Research, Singapore. liugm@comp.nus.edu.sg

Bioinformatics (Oxford, England)
|May 14, 2009
PubMed
Summary
This summary is machine-generated.

We developed an iterative scoring method and the CMC algorithm to accurately predict protein complexes from noisy protein-protein interaction networks. This approach improves prediction accuracy and reduces the impact of experimental errors.

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Mapping Dysfunctional Protein-Protein Interactions in Disease
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Area of Science:

  • * Molecular and Cellular Biology
  • * Bioinformatics
  • * Computational Biology

Background:

  • * Protein complexes are crucial for cellular organization and function.
  • * High-throughput experiments generate vast protein-protein interaction (PPI) data.
  • * PPI data often contains high false positive/negative rates, hindering accurate complex prediction.

Purpose of the Study:

  • * To develop a robust method for predicting protein complexes from PPI networks.
  • * To address the challenge of noise in high-throughput PPI data.
  • * To improve the accuracy and reliability of protein complex identification.

Main Methods:

  • * Developed an iterative scoring method to assign reliability weights to protein pairs in PPI networks.
  • * Created the Clustering-based on Maximal Cliques (CMC) algorithm for protein complex discovery.
  • * CMC identifies maximal cliques and merges/removes overlapping clusters based on interconnectivity.

Main Results:

  • * The iterative scoring method significantly enhances CMC's performance.
  • * The scoring method effectively mitigates the impact of random noise on predictions.
  • * The iterative scoring method also benefits other protein complex prediction tools.
  • * CMC demonstrates effectiveness in protein complex prediction from PPI networks.

Conclusions:

  • * The iterative scoring method is a valuable tool for improving protein complex prediction accuracy.
  • * CMC provides an effective computational approach for identifying protein complexes.
  • * Noise reduction is critical for reliable analysis of PPI data.