Yaser Daanial Khan*, Ebraheem Alzahrani, Wajdi Alghamdi and Malik Zaka Ullah Pages 1 - 10 ( 10 )
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.
Allergen proteins, Prediction, PseAAC, 5-step rule, Statistical Moments
Department of Computer Science, School of Systems and Technology, University of Management and Technology, C II Johar Town, Lahore 54770, Department of Mathematics, Faculty of Science, King Abdulaziz University, P. O. Box 80203, Jeddah 21589, Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, P. O. Box 80221, Jeddah 21589, Department of Mathematics, Faculty of Science, King Abdulaziz University, P. O. Box 80203, Jeddah 21589