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

Mining biological networks for unknown pathways.

Ali Cakmak1, Gultekin Ozsoyoglu

  • 1Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA. ali.cakmak@case.edu

Bioinformatics (Oxford, England)
|September 4, 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

mFLIP: metabolic flux interval prediction.

BMC bioinformatics·2026
Same author

Predicting student grades via adaptive multi-level learning models.

Scientific reports·2026
Same author

When complexity does not pay: benchmarking deep learning and ensemble methods for biomarker discovery.

Briefings in bioinformatics·2026
Same author

BioMark: biomarker analysis tool.

BMC bioinformatics·2026
Same author

MetaboliticsDB: A Database of Metabolomics Analyses.

IEEE transactions on computational biology and bioinformatics·2025
Same author

Exploring the Role of microRNAs as Blood Biomarkers in Alzheimer's Disease and Frontotemporal Dementia.

International journal of molecular sciences·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

This study introduces a novel computational method to predict missing biological pathways using enzyme Gene Ontology (GO) functions. The technique accurately identifies organism-specific pathways, aiding in understanding molecular interactions.

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Genomics

Background:

  • Biological pathways are crucial for understanding molecular interactions but are often incomplete in newly sequenced organisms.
  • Experimental validation of numerous potential pathways is resource-intensive.
  • Comparative genomics tools are needed to predict pathways efficiently.

Purpose of the Study:

  • To develop a computational technique for discovering unknown biological pathways in organisms.
  • To leverage enzyme Gene Ontology (GO) functionalities for pathway prediction.

Main Methods:

  • Modeled pathways as biological functionality graphs based on enzyme GO functions (pathway functionality templates).
  • Identified frequent pathway functionality patterns to infer unknown pathways via pattern matching in metabolic networks.

Related Experiment Videos

  • Experimentally validated the technique on 30 bacterial organisms, predicting 1500 organism-specific pathways.

    • Main Results:

    • Achieved 86% precision and 72% recall for enzyme prediction (nodes) using cross-validation.
    • Demonstrated 85% precision and 64% recall for predicted enzyme relationships.
    • Successfully predicted organism-specific versions of 50 reference pathways.

    Conclusions:

    • The proposed GO-based technique effectively discovers unknown pathways in organisms.
    • This method offers a computationally efficient alternative to experimental validation for pathway discovery.
    • The findings contribute to a better understanding of biological networks and molecular mechanisms.