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 sequence annotation databanks for association patterns.

Irena I Artamonova1, Goar Frishman, Mikhail S Gelfand

  • 1Institute for Bioinformatics, GSF-National Research Center for Environment and Health, Neuherberg, Germany.

Bioinformatics (Oxford, England)
|November 25, 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

From mechanism to substratome: unraveling mysteries of γ-secretase.

The Journal of biological chemistry·2026
Same author

Current status of human endogenous retrovirus annotation.

Briefings in bioinformatics·2026
Same author

Extrusion fountains are hallmarks of chromosome organization emerging upon zygotic genome activation.

Nature communications·2026
Same author

Multi-platform integration of brain and CSF proteomes reveals biomarker panels for Alzheimer's disease.

Briefings in bioinformatics·2026
Same author

MPXV RNA-seq data provide evidence for protection of viral transcripts from APOBEC3 editing.

Journal of virology·2026
Same author

Deciphering the 3D genome organization across species from Hi-C data.

Nucleic acids research·2026
Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

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

This study introduces association rule mining to detect errors in automatic protein annotation. Exceptions to strong rules flag potential errors, improving data quality in bioinformatics databases.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Automatic protein annotation pipelines generate millions of sequences, but contain errors like over/under-predictions and transitive errors.
  • Developing intelligent systems to improve automatically generated annotation quality is a critical bioinformatics challenge.
  • Association rule mining offers a novel approach to detect anomalies in protein annotation items.

Purpose of the Study:

  • To perform a large-scale analysis of association rules from protein annotation databases (Swiss-Prot, PEDANT).
  • To investigate the distribution of rule strengths and identify tendencies.
  • To demonstrate that exceptions to strong association rules can automatically flag annotation errors.

Main Methods:

  • Analysis of association rules derived from Swiss-Prot and PEDANT databases.

Related Experiment Videos

  • Examination of rule strength distributions.
  • Correlation of rule exceptions with annotation error correction dynamics in Swiss-Prot releases.
  • Manual analysis of flagged errors.
  • Compositional breakdown of association rules from PEDANT.

    • Main Results:

    • Revealed novel tendencies in association rule strength distributions: most rules are very strong or very weak.
    • Demonstrated that exceptions to strong rules are significantly enriched in annotation errors.
    • Identified varying strength dependencies of rules across different Swiss-Prot fields.
    • Found that PEDANT errors are mainly related to gene functional roles, while Swiss-Prot errors involve under-annotation.

    Conclusions:

    • Association rule mining is effective for automatically flagging potential protein annotation errors.
    • Exceptions to strong rules serve as reliable indicators of erroneous annotations.
    • Understanding rule strength dependencies aids in identifying specific error types in different databases.