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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,...
Graphs of Two-Variable Functions01:27

Graphs of Two-Variable Functions

A weather map provides a practical example of a function of two variables. Across a wide region such as the United States, temperatures vary from one location to another. Each location can be identified by two geographic coordinates: longitude and latitude. Since a single temperature value is assigned to each coordinate pair, the situation can be represented mathematically as a function with two inputs and one output.In mathematical notation, longitude and latitude can be labeled as x and y,...
Graphs of Equations in Two Variables01:30

Graphs of Equations in Two Variables

An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...
Graphs of Functions01:30

Graphs of Functions

Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all points...

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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

Co-expression networks: graph properties and topological comparisons.

Ramon Xulvi-Brunet1, Hongzhe Li

  • 1Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.

Bioinformatics (Oxford, England)
|November 14, 2009
PubMed
Summary

Gene co-expression networks reveal more about gene regulatory networks than protein-protein interactions or MIPS physical networks. Comparing yeast networks shows distinct structural differences, suggesting limited correlation between gene expression and physical interactions.

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Network Biology

Background:

  • Gene expression data from microarrays are widely used to study biological systems.
  • Gene co-expression networks infer functional relationships and co-regulation.
  • Structural properties of co-expression networks require rigorous comparison with known biological networks.

Purpose of the Study:

  • Investigate structural properties of Saccharomyces Cerevisiae co-expression networks.
  • Compare these properties with yeast transcriptional, MIPS physical, and protein-protein interaction (PPI) networks.

Main Methods:

  • Inference of co-expression networks for Saccharomyces Cerevisiae.
  • Topological comparison of co-expression networks with known yeast biological networks (transcriptional, MIPS physical, PPI).

Main Results:

  • Co-expression networks show significant structural differences from PPI and MIPS physical networks.
  • High gene expression correlation does not strongly correlate with physical protein binding or MIPS interactions.
  • Yeast co-expression networks appear related to the yeast regulatory network.

Conclusions:

  • Gene expression-based co-expression networks better reflect gene regulatory networks.
  • Co-expression networks show less reflection of PPI or MIPS physical interaction networks.