Based on the hypothesis that development of cow's milk allergy (CMA) in children can diagnosed from species of IgE epitope in patients, significant peptide epitope for IgE was analyzed by comprehensive combination analysis using the graphic assessment method. The Adaboost algorithm was used for classification of decision stump as weak classifier, and 6044060 models were constructed. Classification accuracies by 10-fol CV (10-fold cross-validation) were determined. Total distribution of accuracies was depicted and it was analyzed by Gaussian mixture distribution using the EM algorithm. It was found that the distribution could be divided into 5 sub-distributions by the Bayesian information criterion. The Gaussian sub-distributions were grouped into "significant sub-distributions" and "non-significant sub-distributions" and classifiers that belong to "significant distribution" were identified. Analysis of the assignment frequency of individual inputs assigned to the "significant distribution" identified 16 peptide epitopes with high assignment frequency, including aS1C30 and aS1C47 peptides. Since SERYLGYL from aS1C30 has already been reported as significant peptide epitope for CMA diagnose, our proposed method, CC-GAM, was suggested to be a useful tool for selection of significant epitopes.
ASJC Scopus subject areas
- Chemical Engineering(all)