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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,...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Mass Spectrometry: Alkene Fragmentation00:59

Mass Spectrometry: Alkene Fragmentation

Alkenes lose one electron from the unsaturated π bond upon ionization and form stable molecular ions. Further fragmentation of alkenes occurs through three different reaction pathways. The most prominent fragmentation is the cleavage at the allylic position. The resultant allylic carbocation is resonance stabilized. In the mass spectra of terminal alkenes, this fragment appears at a mass-to-charge ratio of 41. In the internal alkenes, where there are two choices of allylic cleavage, the...

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Related Experiment Video

Updated: May 26, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Using MCL to extract clusters from networks.

Stijn van Dongen1, Cei Abreu-Goodger

  • 1European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK. svd@sanger.ac.uk

Methods in Molecular Biology (Clifton, N.J.)
|December 7, 2011
PubMed
Summary
This summary is machine-generated.

The MCL clustering algorithm efficiently analyzes network topology and edge weights for both weighted and unweighted networks. It is scalable and widely used in bioinformatics, with applications in protein sequence similarity and gene expression data.

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Area of Science:

  • Bioinformatics
  • Network Analysis
  • Computational Biology

Background:

  • Network clustering is crucial for understanding complex biological systems.
  • Existing algorithms may not effectively handle both network topology and edge weights.
  • Scalability is a key requirement for analyzing large biological networks.

Purpose of the Study:

  • To present protocols and case studies for the MCL (Markov Cluster) algorithm.
  • To demonstrate the application of MCL in analyzing biological networks.
  • To highlight the algorithm's utility in diverse bioinformatic methods.

Main Methods:

  • The Markov Cluster (MCL) algorithm is detailed.
  • Protocols for clustering networks based on protein sequence similarities are provided.
  • Case studies for clustering networks derived from gene expression profile correlations are presented.

Main Results:

  • The MCL algorithm effectively utilizes both network topology and edge weights.
  • The algorithm demonstrates high scalability for large datasets.
  • Successful applications in analyzing protein and gene expression networks are shown.

Conclusions:

  • MCL is a versatile and powerful clustering algorithm for biological networks.
  • The presented protocols facilitate the application of MCL in bioinformatics.
  • MCL provides robust network analysis for diverse biological data types.