Welcome to iPhoPred

    iPhoPred is a server for the prediction of phosphorylation sites based on machine learning method. In the process of training model, position score function and the correlation information of physicochemcial properties between two residues were considered to build feature sets, and then analysis of variance (ANOVA) was proposed to exclude noise and redundant information. The incremental feature selection (IFS) was used to determine the optimal number of feature which could produce the maximum auROC. Jackknife test results show that the proposed method can discriminate phosphothreonine (T) with auROC of 99.0%, classify phosphotyrosine (Y) with auROC of 99.2% and predict phosphorylated serine (S) with auROC of 90.4%, respectively. We hope the webserver could provide convenience for wet-experimental scholars.