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

SEBINI: Software Environment for BIological Network Inference.

Ronald C Taylor1, Anuj Shah, Charles Treatman

  • 1Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory Richland, WA, USA. ronald.taylor@pnl.gov

Bioinformatics (Oxford, England)
|September 7, 2006
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

Best practices for the LinQ sacroiliac joint stabilization procedure for the treatment of sacroiliac joint disorders.

Pain management·2026
Same author

Developments in dorsal root ganglion stimulation to treat pain of neuropathic origin.

Expert review of neurotherapeutics·2026
Same author

Histotripsy Compared With Microwave Ablation, Radiofrequency Ablation, and Stereotactic Body Radiotherapy for the Treatment of Liver Tumors.

Cureus·2025
Same author

Factors in Surgical Placement and System Design to Reduce Pocket Awareness and Optimize the User Experience of Spinal Cord Stimulators - A Comprehensive Review.

Journal of pain research·2025
Same author

Factors Associated With Next-Day Discharge After Pulmonary Lobectomy.

Annals of thoracic surgery short reports·2025
Same author

Radiomics in Soft Tissue Sarcoma: Toward Precision Imaging in Oncology.

Cancers·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

The Software Environment for BIological Network Inference (SEBINI) offers a unified platform for developing and assessing biological network reconstruction algorithms. This tool aids researchers in analyzing experimental data and refining inference methods for more efficient network analysis.

Area of Science:

  • Computational Biology
  • Bioinformatics

Background:

  • Biological network inference is crucial for understanding cellular processes.
  • Existing methods for network reconstruction require diverse tools and frameworks.
  • Evaluating and comparing these methods is challenging.

Purpose of the Study:

  • To introduce the Software Environment for BIological Network Inference (SEBINI).
  • To provide an integrated platform for deploying, evaluating, and comparing network inference algorithms.
  • To facilitate the analysis of both simulated and experimental biological data.

Main Methods:

  • SEBINI integrates algorithm deployment and evaluation within a single environment.
  • It supports training and comparison of inference methods using artificial networks and simulated data.

Related Experiment Videos

  • The platform enables analysis of high-throughput experimental expression data.

    • Main Results:

    • SEBINI facilitates the comparison and training of various network inference algorithms.
    • It allows for the analysis of experimental biological data using trained inference methods.
    • The environment streamlines the process of biological network reconstruction.

    Conclusions:

    • SEBINI enhances the accuracy and efficiency of biological network reconstruction.
    • It serves as a valuable tool for software developers and bioinformaticians.
    • The platform reduces the effort and time required for network analysis.