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Allergy is an overreaction by the immune system to a previously encountered, ordinarily harmless substance —typically proteins—resulting in skin rash, swelling of mucous membranes, sneezing or wheezing, or other abnormal conditions. The use of modified proteins is increasingly widespread: their presence in food, commercial products, such as washing powder, and medical therapeutics and diagnostics, makes predicting and identifying potential allergens a crucial societal issue. The prediction of allergens has been explored widely using bioinformatics, with many tools being developed in the last decade; many of these are freely available online. Here, we report a set of novel models for allergen prediction utilizing amino acid E -descriptors, auto- and cross-covariance transformation, and several machine learning methods for classification, including logistic regression (LR), decision tree (DT), naïve Bayes (NB), random forest (RF), multilayer perceptron (MLP) and k nearest neighbours ( k NN). The best performing method was k NN with 85.3 % accuracy at 5-fold cross-validation. The resulting model has been implemented in a revised version of the AllerTOP server ( http://www.ddg-pharmfac.net/AllerTOP ). Figure ᅟ

Doc number: P7