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 Experiment Videos

An algorithm for clustering cDNA fingerprints.

E Hartuv1, A O Schmitt, J Lange

  • 1Department of Computer Science, Tel-Aviv University, Tel-Aviv, 69978, Israel.

Genomics
|June 30, 2000
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Modeling reticular and ventral ruminal pH of lactating dairy cows using ingestion and rumination behavior.

Journal of dairy science·2020
Same author

Hepatic gene expression variations in response to high-fat diet-induced impaired glucose tolerance using RNAseq analysis in collaborative cross mouse population.

Mammalian genome : official journal of the International Mammalian Genome Society·2019
Same author

Effect of Linearization in a WNT Signaling Model.

Computational and mathematical methods in medicine·2019
Same author

Inter- and intra-observer reliability of animal welfare indicators for the on-farm self-assessment of fattening pigs.

Animal : an international journal of animal bioscience·2019
Same author

The bromodomain protein BRD4 regulates the KEAP1/NRF2-dependent oxidative stress response.

Cell death & disease·2014
Same author

Genomewide study and validation of markers associated with production traits in German Landrace boars.

Journal of animal science·2014
Same journal

Integrating transcriptomics and metabolomics reveals the molecular landscape of sperm maturation driven by regional differentiation in the epididymis of Guizhou-Guiqian semi-fine wool sheep.

Genomics·2026
Same journal

Impact of genotype on histopathology and clinical characters in a Chinese cohort with obstructive hypertrophic cardiomyopathy.

Genomics·2026
Same journal

A novel reusable transcriptome-wide association study workflow used to map key genes linked to important cattle traits.

Genomics·2026
Same journal

The large mitochondrial genome of Syndiclis anlungensis (Lauraceae): Genome structure, comparative analysis, and phylogenetic relationships with other Syndiclis species.

Genomics·2026
Same journal

DeepGEP: Deep learning for gene expression prediction from multi-omics in mammals.

Genomics·2026
Same journal

Molecular features of external Auditory Canal cholesteatoma by microbial metagenomic sequencing.

Genomics·2026
See all related articles

We developed a new graph-based algorithm for clustering gene expression data. This method efficiently identifies gene clones without assuming hierarchical structures or the number of clusters, outperforming existing techniques.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Clustering large datasets is a key challenge in gene expression analysis.
  • Analyzing cDNA fingerprints from oligonucleotide hybridization helps identify genes.

Purpose of the Study:

  • To develop a novel algorithm for cluster analysis of gene expression data.
  • To overcome limitations of existing methods, such as assumptions of hierarchical structure and prior knowledge of cluster numbers.

Main Methods:

  • Developed a new algorithm based on graph theoretic techniques for cluster analysis.
  • Applied the algorithm to analyze cDNA fingerprints from gene expression data.

Main Results:

  • The algorithm demonstrated high speed and robustness to high error rates.

Related Experiment Videos

  • Outperformed the Greedy method in tests with simulated libraries.
  • Achieved good solution quality in a blind test on real cDNA fingerprints.

    • Conclusions:

    • The novel graph-based algorithm offers an effective approach for clustering gene expression data.
    • The method is efficient, robust, and does not require prior assumptions about cluster structure or number.