Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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...
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...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Tumor-Intrinsic Hepatocyte Arm-Level Genomic States Shape Immunotherapy Response Heterogeneity in Hepatocellular Carcinoma.

Cancer research and treatment·2026
Same author

Targeting sFRP1 with WAY-316606 Suppresses Proliferation, Migration, and Invasion in Metastatic Melanoma.

Cancers·2026
Same author

Molecular analysis of squalene epoxidase gene mutations in Trichophyton rubrum from clinical onychomycosis samples in South Korea.

Scientific reports·2026
Same author

Delineation of the heterogeneity underlying genomic instability in hereditary breast cancers reveals four disease subtypes.

Experimental & molecular medicine·2026
Same author

Comparison of hybrid-and mono-pathotype Escherichia coli isolates from South Korea based on whole genome analysis and cytotoxicity assay.

Journal of biomedical science·2026
Same author

Explainable Machine Learning for Assessing Digital Health Literacy in Older Adults: Validation and Development of a Two-Stage Model Integrating Performance-Based and Self-Assessed Indicators.

JMIR medical informatics·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jul 3, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

PathCluster: a framework for gene set-based hierarchical clustering.

Tae-Min Kim1, Seon-Hee Yim, Yong-Bok Jeong

  • 1Department of Microbiology and Integrated Research Center for Genome Polymorphism, College of Medicine, The Catholic University of Korea, Seoul 137-701, Korea.

Bioinformatics (Oxford, England)
|July 17, 2008
PubMed
Summary
This summary is machine-generated.

PathCluster software integrates gene clustering and gene set analysis for expression profiles. It provides valuable biological insights and testable hypotheses by visualizing gene set relationships.

Related Experiment Videos

Last Updated: Jul 3, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Gene clustering and gene set-based functional analysis are standard methods for analyzing expression profiles.
  • Combining these methods offers potential for deeper biological insights.

Purpose of the Study:

  • To develop a comprehensive method that jointly combines gene clustering and gene set-based functional analysis.
  • To create a software package facilitating the integration of these analytical approaches.

Main Methods:

  • Developed PathCluster, a software package for gene set-based clustering.
  • Utilized an agglomerative hierarchical clustering algorithm.
  • Visualized relationships between gene sets using a dendrogram.

Main Results:

  • PathCluster enables visual assessment of gene set relationships.
  • Facilitates obtaining biological insights into molecular functions, motif synergy, and biological processes.
  • Allows interrogation of relationships between biological themes like function-versus-regulatory motif or drug-versus-function.
  • Applicable for knowledge-based sample partitioning and clinical class categorization.

Conclusions:

  • PathCluster enhances the gleaning of meaningful biological insights from expression profiles.
  • Facilitates the generation of testable hypotheses.
  • Offers extended applicability for various biological and clinical analyses.