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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...
Proteomics01:33

Proteomics

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.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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Related Experiment Video

Updated: Jun 9, 2026

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

Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data.

Zhu-Hong You1, Ying-Ke Lei, Jie Gui

  • 1Intelligent Computing Laboratory, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China.

Bioinformatics (Oxford, England)
|September 7, 2010
PubMed
Summary
This summary is machine-generated.

A new manifold embedding technique reliably assesses protein-protein interaction (PPI) data and predicts new interactions using only network topology. This method excels with sparse PPI networks, overcoming limitations of traditional algorithms for biological discoveries.

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Last Updated: Jun 9, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions
08:07

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

Published on: August 2, 2015

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • High-throughput protein-protein interaction (PPI) data is crucial for understanding cellular processes but suffers from unreliability and sparseness.
  • Existing computational methods for assessing and predicting PPIs often require multiple data sources or are hindered by sparse network data.

Purpose of the Study:

  • To develop a computational approach for assessing PPI reliability and predicting novel interactions.
  • To address the limitations of existing methods, particularly in handling sparse protein interactome data.

Main Methods:

  • Developed a manifold embedding technique utilizing Isometric Feature Mapping (ISOMAP).
  • Transformed PPI networks into a low-dimensional metric space to assess and predict interactions based on point similarity.
  • Assigned a reliability index to protein pairs based on embedded space similarity.

Main Results:

  • The method effectively assesses interaction reliability and predicts new interactions using only PPI network topology.
  • Demonstrated high-functional homogeneity and localization coherence for top-ranked interactions.
  • Showed significant efficiency and success on large, sparse PPI networks where traditional algorithms falter.

Conclusions:

  • The proposed manifold embedding technique offers a robust solution for dealing with unreliable and sparse PPI data.
  • This algorithm is a promising tool for identifying both false positive and false negative interactions in PPI networks.
  • The method enhances the quality of PPI maps, facilitating deeper biological discoveries.