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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,...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
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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Related Experiment Video

Updated: May 16, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

A fast ranking algorithm for predicting gene functions in biomolecular networks.

Matteo Re1, Marco Mesiti, Giorgio Valentini

  • 1Dipartimento di Informatica, Università degli Studi di Milano, Via Comelico 39/41, I-20135 Milano, Italia. re@di.unimi.it

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|December 11, 2012
PubMed
Summary

We developed a novel gene ranking method using kernelized score functions to efficiently predict gene function in biological networks. This approach significantly improves accuracy and reduces computational time for complex biological network analysis.

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A Protocol for Computer-Based Protein Structure and Function Prediction
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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Ranking genes by biological function in networks is computationally challenging.
  • Existing methods struggle with performance and complexity.

Purpose of the Study:

  • To develop a transductive gene ranking method addressing performance and complexity issues.
  • To improve gene function prediction in biomolecular networks.

Main Methods:

  • Utilized a transductive learning approach with kernelized score functions.
  • Exploited network topology and graph structure of biomolecular networks.
  • Integrated multiple biomolecular data sources for network construction.

Main Results:

  • Achieved significantly better results than state-of-the-art network-based algorithms for gene function prediction.
  • Demonstrated substantial savings in computational time.
  • Successfully applied to a yeast network integrating diverse data sources.

Conclusions:

  • The proposed method effectively ranks genes in functional networks.
  • It offers a general, fast, and accurate solution for gene function prediction.
  • Potential for application to other node ranking problems in large biological networks.