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Allergic Reactions02:06

Allergic Reactions

Overview

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Related Experiment Video

Updated: May 21, 2026

Application of Biochip Microfluidic Technology to Detect Serum Allergen-specific Immunoglobulin E (sIgE)
07:10

Application of Biochip Microfluidic Technology to Detect Serum Allergen-specific Immunoglobulin E (sIgE)

Published on: April 21, 2019

SORTALLER: predicting allergens using substantially optimized algorithm on allergen family featured peptides.

Lida Zhang1, Yuyi Huang, Zehong Zou

  • 1Plant Biotechnology Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200030, China.

Bioinformatics (Oxford, England)
|June 14, 2012
PubMed
Summary
This summary is machine-generated.

SORTALLER accurately classifies allergens using allergen family featured peptides (AFFPs) and support vector machines (SVM). This tool achieves high specificity and sensitivity, outperforming existing software for allergen identification.

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Last Updated: May 21, 2026

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07:10

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Published on: April 21, 2019

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09:09

Removal and Replacement of Endogenous Ligands from Lipid-Bound Proteins and Allergens

Published on: February 24, 2021

Humanized Mediator Release Assay as a Read-Out for Allergen Potency
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Humanized Mediator Release Assay as a Read-Out for Allergen Potency

Published on: June 29, 2021

Area of Science:

  • Bioinformatics
  • Immunology
  • Computational Biology

Background:

  • Allergen identification is crucial for managing allergic diseases.
  • Existing methods for allergen classification can be limited in accuracy and efficiency.

Purpose of the Study:

  • To develop and evaluate SORTALLER, an advanced online tool for classifying allergenic proteins.
  • To improve the accuracy and speed of allergen discrimination using a novel feature set.

Main Methods:

  • Utilized allergen family featured peptides (AFFPs) and normalized BLAST E-values to create feature vectors.
  • Employed a support vector machine (SVM) classification model.
  • Validated performance on independent datasets of diverse protein sequences.

Main Results:

  • SORTALLER achieved high specificity (98.4%) and sensitivity (98.6%).
  • Demonstrated superior performance compared to existing allergen classification software.
  • Attained a high Matthews correlation coefficient (MCC) of 0.970.
  • Exhibited fast running speed for batch processing of amino acid sequences.

Conclusions:

  • SORTALLER is a highly accurate and efficient online tool for allergen classification.
  • The AFFP dataset and SVM approach provide a robust method for discriminating allergenic proteins.
  • SORTALLER offers a valuable resource for researchers and clinicians in allergy and immunology.