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

Protein structure and fold prediction using tree-augmented naive Bayesian classifier.

A Chinnasamy1, W K Sung, A Mittal

  • 1Department of Computer Science, National University of Singapore, 3 Science Drive 2, Singapore 117543. arun@ksung.comp.nus.edu.sg

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|March 3, 2004
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

Flooded with potential: urban drainage science as seen by early-career researchers.

Water science and technology : a journal of the International Association on Water Pollution Research·2025
Same author

Uninostril endonasal trans-sphenoidal approach for pituitary macroadenoma-Operative nuances step by step.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia·2024
Same author

Retraction Note: IKBKE is induced by STAT3 and tobacco carcinogen and determines chemosensitivity in non-small cell lung cancer.

Oncogene·2024
Same author

Correlation of Transient Elastography with Liver Iron Concentration and Serum Ferritin Levels in Patients with Transfusion-Dependent Thalassemia Major from Oman.

Mediterranean journal of hematology and infectious diseases·2023
Same author

Defining the rate of nutritional and metabolic derangements after pancreatic resection.

Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]·2022
Same author

Dose-dense Paclitaxel and Carboplatin as Neoadjuvant Chemotherapy for Stage IIB/IIIA Non-small Cell Lung Cancer - A Phase II trial.

Clinical oncology (Royal College of Radiologists (Great Britain))·2021
Same journal

Trust, Reproducibility, and Progress: The Roles of Independent Blind Prediction and Assessment and Benchmarking in Computational Biology.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

The Evolving Cyberinfrastructure at the National Institutes of Health to Support Data and AI in Biomedical Research.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

Applications of AI & ML in Biomanufacturing of Cell and Gene Therapies.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

AI for Health: Leveraging Artificial Intelligence to Revolutionize Healthcare.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

Workshop Introduction: Advances of AI Methods in Single Cell Spatial Omics.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

DRIVE-KG: Enhancing variant-phenotype association discovery in understudied complex diseases using heterogeneous knowledge graphs.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
See all related articles

This study introduces a novel Tree-Augmented Networks (TAN) framework for protein structure classification. This computational approach enhances accuracy in determining protein structure and fold classes.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning

Background:

  • Determining protein structure class and fold class is crucial in bioinformatics.
  • Large datasets necessitate efficient computer-based techniques for protein structure analysis.
  • Existing methods like neural networks and SVMs have limitations in handling complex protein data.

Purpose of the Study:

  • To present a novel framework for protein structure and fold class determination using Tree-Augmented Networks (TAN).
  • To enhance the performance of TAN through data pre-processing and post-processing techniques.
  • To provide an intuitive and interpretable model for understanding protein structure features.

Main Methods:

  • Utilized Tree-Augmented Networks (TAN), a Bayesian network learning approach with fewer assumptions than naive Bayes.

Related Experiment Videos

  • Implemented feature discretization for data pre-processing to improve TAN performance.
  • Applied a Mean Probability Voting (MPV) scheme for post-processing to refine predictions.

    • Main Results:

    • The TAN-based framework demonstrated effectiveness in protein structure and fold class determination.
    • Experimental results validated the framework's performance against existing methods on two databases.
    • The intuitive nature of the Bayesian approach allowed for the assessment of feature significance (e.g., hydrophobicity) for each class.

    Conclusions:

    • The developed TAN framework offers an effective computational solution for protein structure classification.
    • The BAYESPROT web server provides accessible implementation of this novel approach.
    • This method aids in understanding the complex relationships between protein features and structural classes.