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

Biological network comparison using graphlet degree distribution.

Natasa Przulj1

  • 1Computer Science Department, University of California, Irvine, CA 92697-3425, USA. natasha@ics.uci.edu

Bioinformatics (Oxford, England)
|January 24, 2007
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

Combining graphlets and random walks for capturing complex network topology.

Scientific reports·2026
Same author

MONFIT: multi-omics factorization-based integration of time-series data sheds light on Parkinson's disease.

NAR molecular medicine·2025
Same author

Biological databases in the age of generative artificial intelligence.

Bioinformatics advances·2025
Same author

Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data.

Bioinformatics (Oxford, England)·2025
Same author

Simplicity within biological complexity.

Bioinformatics advances·2025
Same author

Author Correction: Systematic protein-protein interaction mapping for clinically relevant human GPCRs.

Molecular systems biology·2025
Same journal

Probabilistic RNA designability via interpretable ensemble approximation and dynamic decomposition.

Bioinformatics (Oxford, England)·2026
Same journal

Quantifying domain-specific relevance of computational biology Wikipedia articles using TF-IDF and cosine similarity.

Bioinformatics (Oxford, England)·2026
Same journal

GATSBI: improving context-aware protein embeddings through biologically motivated data splits.

Bioinformatics (Oxford, England)·2026
Same journal

BiMba: using Vision Mamba to predict protein sites that bind other proteins.

Bioinformatics (Oxford, England)·2026
Same journal

ProMeta: a meta-learning framework for robust disease diagnosis and prediction from plasma proteomics.

Bioinformatics (Oxford, England)·2026
Same journal

Is a Win-Win possible? Achieving pareto-optimal privacy-utility balance in fine-tuned genome language model embeddings against embedding reconstruction attacks.

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

Comparing biological networks is crucial for understanding cellular processes. This study introduces a novel graphlet degree distribution method to quantify network similarity, finding eukaryotic protein-protein interaction networks better fit geometric random graphs.

Area of Science:

  • Network biology
  • Computational biology
  • Systems biology

Background:

  • Comparing biological networks is vital for insights into cellular mechanisms and therapeutics.
  • Existing heuristics like degree distribution and clustering coefficient are insufficient for comprehensive network similarity assessment.
  • Demonstrating network similarity is computationally challenging due to the vast number of properties to analyze.

Purpose of the Study:

  • To develop a systematic and robust measure for comparing the local structure of biological networks.
  • To introduce a generalized graphlet degree distribution for quantifying network similarity with numerous constraints.
  • To evaluate the performance of different network models in representing eukaryotic protein-protein interaction (PPI) networks.

Main Methods:

Related Experiment Videos

  • Generalized the concept of degree distribution to graphlet degree distributions, counting nodes connected to k graphlets.
  • Utilized 73 graphlet degree distributions for graphlets of size 2-5 nodes as a comprehensive measure of local network structure.
  • Developed a network 'agreement' measure (0-1) to quantify the similarity between graphlet degree distributions of two networks.

Main Results:

  • The proposed method provides a systematic measure of local network structure, imposing a large number of similarity constraints.
  • The network agreement measure effectively quantifies the similarity between biological networks.
  • Analysis of 14 eukaryotic PPI networks revealed they are better modeled by geometric random graphs than by scale-free or Erdős-Rényi models.

Conclusions:

  • The graphlet degree distribution offers a powerful and extensible approach for comparing biological networks.
  • The developed network agreement measure facilitates robust similarity assessments.
  • Geometric random graphs provide a more accurate model for eukaryotic PPI networks compared to traditional network models.