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Related Experiment Video

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

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Published on: April 21, 2019

ProAll-D: protein allergen detection using long short term memory - a deep learning approach.

Pallavi M Shanthappa¹, Rakshitha Kumar¹

¹Department of Computer Science, Amrita School of Arts and Sciences, Mysuru Campus, Amrita Vishwa Vidyapeetham, India.

|September 22, 2022

Summary

Predicting food allergens is crucial for public health. A deep learning model, Long Short-Term Memory (LSTM), achieved 91.5% accuracy in identifying allergenic proteins, outperforming traditional methods.

Keywords:

ACC transformation ADA boost Allergen prediction Bagging classifier Classifier Extra tree classifier Gaussian naive bayes LSTM model Linear discriminant analysis Quadratic discriminant analysis

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Last Updated: Aug 28, 2025

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Bioinformatics
Computational Biology
Immunology

Background:

Allergic reactions stem from the immune system's overresponse to typically harmless molecules, often proteins.
These reactions manifest in various symptoms like rashes, asthma, and swelling.
Traditional bioinformatics methods for allergy prediction have shown limited effectiveness.

Purpose of the Study:

To develop a more accurate method for predicting allergenic proteins.
To overcome the limitations of existing machine learning and sequence similarity approaches.
To identify potential allergens in dietary proteins.

Main Methods:

Utilized a deep learning model, Long Short-Term Memory (LSTM).
Employed 2,427 known allergens and 2,427 non-allergens from the Central Science Laboratory and NCBI.
Described protein sequences using five E-descriptors (hydrophilicity, length, helix propensity, abundance/dispersion, beta-strand propensity) and ACC transformation.
Trained and tested the model on an 80:20 data split.

Main Results:

The LSTM model achieved a prediction accuracy of 91.5%.
This performance surpassed other evaluated machine learning techniques, including Extra Tree Classifier (90%), Bagging Classifier (85.8%), and Quadratic Discriminant Analysis (84.2%).
Gaussian Naive Bayes and Radius Neighbour's Classifier showed lower accuracies at 64.14% and 49.2%, respectively.

Conclusions:

The LSTM model demonstrates superior performance in predicting protein allergenicity.
A web server, ProAll-D, was developed using the LSTM approach for novel allergen identification.
The ProAll-D server and associated data are publicly accessible.