TITLE

AllerTOP v.2-a server for in silico prediction of allergens

AUTHOR(S)
Dimitrov, Ivan; Bangov, Ivan; Flower, Darren; Doytchinova, Irini
PUB. DATE
June 2014
SOURCE
Journal of Molecular Modeling;Jun2014, Vol. 20 Issue 6, p1
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
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 ( kNN). The best performing method was kNN with 85.3 % accuracy at 5-fold cross-validation. The resulting model has been implemented in a revised version of the AllerTOP server (). [Figure not available: see fulltext.]
ACCESSION #
96773679

 

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