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Related Concept Videos

Phylogenetic Trees03:21

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the...
Phylogenetic Trees03:21

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Phylogeny01:23

Phylogeny

Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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...

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Related Experiment Video

Updated: Jul 10, 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

Hierarchical tree snipping: clustering guided by prior knowledge.

Dikla Dotan-Cohen1, Avraham A Melkman, Simon Kasif

  • 1Department of Computer Science, Ben Gurion University, Beer Sheva 84105, Israel. dotna@cs.bgu.ac.il

Bioinformatics (Oxford, England)
|November 9, 2007
PubMed
Summary
This summary is machine-generated.

This study introduces partitioning by snipping, a novel method to improve gene clustering. It enhances biological significance by integrating background knowledge for more coherent gene groupings.

More Related Videos

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Related Experiment Videos

Last Updated: Jul 10, 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

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Hierarchical clustering is a common gene grouping method based on expression similarity.
  • Standard hierarchical clustering often yields clusters lacking significant functional coherence.

Purpose of the Study:

  • To develop a new framework, partitioning by snipping, to enhance the biological significance of gene clusters.
  • To create algorithms for functional gene prediction and discovery of functionally enriched co-expressed gene clusters.

Main Methods:

  • Introduced a novel framework called partitioning by snipping.
  • Developed algorithms that cut selected edges at variable levels to induce clusters.
  • Selected edges to maximize consistency with background knowledge like functional classifications.

Main Results:

  • Algorithms demonstrated good performance in functional gene prediction and co-expressed gene cluster discovery.
  • The method performed well even when the actual number of clusters deviated from the requested number.
  • Achieved improved performance compared to previously proposed algorithms.

Conclusions:

  • Partitioning by snipping offers a more biologically significant approach to gene clustering.
  • The developed algorithms are effective for gene function prediction and identifying functionally enriched gene sets.
  • A Java package for TreeSnipping is available for practical application.