A Comparative Analysis of Novel Deep Learning and Ensemble Learning Models to Predict the Allergenicity of Food Proteins

Abstract

Traditional food allergen identification mainly relies on in vivo and in vitro experiments, which often needs a long period and high cost. The artificial intelligence (AI)-driven rapid food allergen identification method has solved the two drawbacks and is becoming an efficient auxiliary tool. Aiming to overcome the limitations of lower accuracy of traditional machine learning models in predicting the allergenicity of food allergens, this work proposed to introduce transformer deep learning model with self-attention mechanism and ensemble learning model (representative as Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting (XGBoost)) to solve the problem. In order to highlight the superiority of the proposed novel method, the study also selected various commonly used machine learning models as the baseline classifiers. The results of 5-fold cross-validation found that the AUC of the deep model was the highest (0.9400), which was better than the ensemble learning and baseline algorithms. But it needed to be pre-trained, and the training cost was highest. By comparing the characteristics of transformer model and boosting models, it can be analyzed that the two types of models have their own advantages, which provides novel clues and inspiration for the rapid prediction of food allergens in the future.