ProAll-D

Bioinformatics has been extensively utilized to predict allergens, involving a range of tools and methodologies. Even though discriminative algorithms such as support vector machines (SVM) have recently improved alignment methods, they are time-consuming if a new pattern must be compared to a huge amount of existing data. Another limitation of the alignment methods is that they won't develop a model of the true positive but rather estimate the average distances between protein sequences.

We present a set of novel allergen prediction models based on protein characteristic values, ACC (auto and cross-covariance) transformation, and a variety of supervised learning, ensemble learning, and deep learning classification methods such as Gaussian Nave Bayes, Radius Neighbour’s Classifier, Extra Tree Classifier, Bagging Classifier, ADA Boost, Linear Discriminant Analysis, Quadratic Discriminant Analysis, and LSTM methods.

LSTM was the best-performing model, with an accuracy of 91.51 percent.