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

Predicting allergenic proteins using wavelet transform.

Kuo-Bin Li1, Praveen Issac, Arun Krishnan

  • 1Bioinformatics Institute, 30 Biopolis Street, Singapore 138671, Singapore. kuobin@bii.a-star.edu.sg

Bioinformatics (Oxford, England)
|May 1, 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

Young Professionals in Neuroradiology: Opportunities, Challenges, and Future Directions.

AJNR. American journal of neuroradiology·2024
Same author

Oral Paracetamol vs Oral Ibuprofen in Patent Ductus Arteriosus: A Randomized, Controlled, Noninferiority Trial.

The Journal of pediatrics·2020
Same author

Predicting Protein-Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids.

International journal of molecular sciences·2016
Same author

Endovascular Reperfusion and Cooling in Cerebral Acute Ischemia (ReCCLAIM I).

Journal of neurointerventional surgery·2013
Same author

Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou's pseudo amino acid composition.

Journal of theoretical biology·2012
Same author

Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis.

Bioinformatics (Oxford, England)·2011
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

Predicting food and protein allergenicity is crucial. This study introduces a novel system combining sequence similarity and motif-based methods, achieving over 90% precision in identifying potential allergens.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Immunology

Background:

  • Predicting protein allergenicity is vital due to increasing transgenic protein use.
  • Existing methods rely on sequence similarity or known allergen motifs, with limitations in precision or recall.
  • A combined approach offers improved accuracy for allergenicity prediction.

Purpose of the Study:

  • To develop a novel system for predicting protein allergenicity.
  • To integrate similarity-based and motif-based prediction strategies.
  • To enhance the accuracy and reliability of allergenicity assessments.

Main Methods:

  • Utilized a clustering algorithm to group known allergenic proteins.
  • Employed wavelet analysis and multiple sequence alignment to identify conserved motifs.

Related Experiment Videos

  • Developed hidden Markov model (HMM) profiles for identified motifs.
  • Created a supplementary database for allergens lacking detectable motifs.
  • Applied BLASTP for comparisons against the supplementary database.

    • Main Results:

    • Achieved over 70% recall and over 90% precision in cross-validation experiments.
    • Identified approximately 2000 potential allergens within the Swiss-Prot database.
    • Demonstrated the efficacy of the combined prediction system.

    Conclusions:

    • The integrated system effectively predicts protein allergenicity.
    • This approach offers a significant improvement over traditional methods.
    • The developed tool aids in identifying potential allergens in large protein datasets.