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

Functional annotation from predicted protein interaction networks.

Jason McDermott1, Roger Bumgarner, Ram Samudrala

  • 1Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.

Bioinformatics (Oxford, England)
|May 28, 2005
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 cutting edge computational and experimental methods for targeting KRAS mutations in non-small cell lung cancer.

Expert opinion on drug discovery·2026
Same author

Multiscale analysis and optimal glioma therapeutic candidate discovery using the CANDO platform.

Journal of cheminformatics·2026
Same author

Uncovering novel therapeutics for schizophrenia: a multitarget approach using the CANDO platform.

bioRxiv : the preprint server for biology·2026
Same author

Deciphering bisphenol A (BPA)-elicited osteoarthritis mechanisms through network toxicology and molecular docking, then de novo generation of novel therapeutic candidates.

BMC pharmacology & toxicology·2025
Same author

Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms.

Bioinformatics (Oxford, England)·2025
Same author

Accurate informatic modeling of tooth enamel pellicle interactions by training substitution matrices with Mat4Pep.

Frontiers in materials·2025
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

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

Computational methods can predict protein interactions, enabling functional annotation across many organisms. This approach matches the accuracy of experimental data, aiding in the discovery of new protein functions.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Experimental determination of protein-protein interaction networks is resource-intensive.
  • Computational methods can predict protein interactions using data from other genomes.
  • Network connectivity analysis offers innovative functional inference.

Purpose of the Study:

  • To evaluate a network-based functional annotation method using predicted protein interaction networks.
  • To assess the performance of computational predictions against experimental data.
  • To apply the method to a large number of organisms for functional annotation.

Main Methods:

  • Utilized predicted protein interaction networks derived from computational methods.
  • Employed a network-based functional annotation approach.

Related Experiment Videos

  • Applied the method across predicted networks for over 50 organisms.

    • Main Results:

    • The network-based functional annotation method performed equally well on predicted and experimentally derived interaction networks.
    • The approach was effective for both manually and computationally assigned annotations.
    • Provided functional annotations for numerous unannotated proteins and validated existing low-confidence annotations across diverse organisms.

    Conclusions:

    • Computational prediction of protein interaction networks is a viable and effective strategy for functional inference.
    • Network-based functional annotation methods are robust and applicable to both predicted and experimental data.
    • This study significantly expanded functional annotations for proteins across all domains of life.