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 naïve Bayesian classifier.

Arunkumar Chinnasamy1, Wing-Kin Sung, Ankush Mittal

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

Journal of Bioinformatics and Computational Biology
|August 4, 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

The haplotype-resolved T2T genome for Bauhinia × blakeana sheds light on the genetic basis of flower heterosis.

GigaScience·2025
Same author

UniVar: A variant interpretation platform enhancing rare disease diagnosis through robust filtering and unified analysis of SNV, INDEL, CNV and SV.

Computers in biology and medicine·2024
Same author

SurVIndel2: improving copy number variant calling from next-generation sequencing using hidden split reads.

Nature communications·2024
Same author

Constructing telomere-to-telomere diploid genome by polishing haploid nanopore-based assembly.

Nature methods·2024
Same author

PlantCFG: A comprehensive database with web tools for analyzing candidate flowering genes in multiple plants.

Plant communications·2023
Same author

Integrated 3D genome, epigenome and transcriptome analyses reveal transcriptional coordination of circadian rhythm in rice.

Nucleic acids research·2023
Same journal

CNV-ECOD: A copy number variation detection method based on ECOD algorithm using next-generation sequencing data.

Journal of bioinformatics and computational biology·2026
Same journal

ReinVar: A model-free paradigm-based reinforcement learning approach to detect copy number variation.

Journal of bioinformatics and computational biology·2026
Same journal

When pipelines run but coordinates fail: A simple spatial specificity check for false locality in post-GWAS analysis.

Journal of bioinformatics and computational biology·2026
Same journal

Comparative benchmarking of template-based, evolutionary-diffusion, and generative language models for IsPETase structure prediction.

Journal of bioinformatics and computational biology·2026
Same journal

Trap spaces as labelled ideals of SCC posets: A structural-functional theory of reachability in asynchronous boolean networks.

Journal of bioinformatics and computational biology·2026
Same journal

Erratum - DDINet: Drug-drug interaction prediction network based on multi-molecular fingerprint features and multi-head attention centered weighted autoencoder.

Journal of bioinformatics and computational biology·2026
See all related articles

This study introduces a Tree-Augmented Bayesian Network (TAN) framework for classifying protein structure and fold classes from sequence data. The TAN approach offers improved accuracy over other methods, aiding in understanding protein complexity.

Area of Science:

  • Computational biology
  • Bioinformatics
  • Structural bioinformatics

Background:

  • Classifying protein structure and fold classes from vast sequence data is crucial.
  • Existing methods often rely on multiple binary classifiers, leading to computational inefficiency.
  • Machine learning techniques like Neural Networks and Support Vector Machines (SVMs) have been applied.

Purpose of the Study:

  • To present a novel framework for multi-classification of protein structure and fold classes using Tree-Augmented Bayesian Networks (TAN).
  • To enhance TAN performance through data pre-processing (discretization) and post-processing (Mean Probability Voting).
  • To provide an intuitive Bayesian approach for understanding feature significance in protein structure classification.

Main Methods:

Related Experiment Videos

  • Utilized Tree-Augmented Bayesian Networks (TAN) for direct multi-classification.
  • Implemented an improved feature vector representation based on Ding et al. (2001).
  • Applied feature discretization for data pre-processing and Mean Probability Voting (MPV) for post-processing to enhance TAN performance.

    • Main Results:

    • The TAN framework demonstrated higher accuracy compared to other discriminative methods on benchmark datasets.
    • The Bayesian network structure provides intuitive insights into feature significance (e.g., hydrophobicity) for each protein class.
    • Experimental results validated the effectiveness of the proposed approach.

    Conclusions:

    • The TAN framework offers an accurate and interpretable method for protein structure and fold class determination.
    • The approach enhances understanding of protein structural complexity through feature significance analysis.
    • The BAYESPROT web server provides access to the implemented framework and detailed results.