关键词: 预测, 糖基化, 蛋白质, 核fisher判别分析（KFDA）, 特征

Abstract: To predict the O-glycosylation sites in protein sequence, the method of Kernel Fisher Discriminant Analysis (KFDA) was proposed under various window sizes. Encoded by the sparse coding, the samples were first mapped onto a feature space implicitly defined by a kernel function, and then they were classified into two classes in the feature space by Fisher discriminant analysis. Furthermore, the majority-vote scheme was used to combine all the pre-classifiers to improve the prediction performance. The results indicate that the performance of ensembles of KFDA is better than that of FDA, PCA and pre-classifier. The prediction accuracy is about 86.5%.

Key words: prediction, glycosylation, protein, kernel fisher discriminant analysis, feature