Background: Allergens are antigens that can stimulate an atopic type I human hypersensitivity reaction by
an immunoglobulin E (IgE) reaction. Some proteins are naturally allergenic than others. The challenge for toxicologists
is to identify properties that allow proteins to cause allergic sensitization and allergic diseases. The identification of
allergen proteins is a very critical and pivotal task. The experimental identification of protein functions is a hectic,
laborious and costly task; therefore, computer scientists have proposed various methods in the field of computational
biology and bioinformatics using various data science approaches. Objectives: Herein, we report a novel predictor for
identification of Allergen proteins.
Methods: For feature extraction, statistical moments and various position-based features have been incorporated into
Chou’s pseudo amino acid composition (PseAAC), and are used for training of a neural network.
Results: The predictor is validated through 10-fold cross-validation and Jackknife testing, which gave 99.43% and
99.87% accurate results.
Conclusions: Thus, the proposed predictor can help in predicting the Allergen proteins in an efficient and accurate
way and can provide baseline data for the discovery of new drugs and biomarkers.