Since the SVM models trained with various features, the most accurate model of each kinase-specific phosphorylation sites are selected and used to implement a prediction server. As shown in Table S3, the trained features, SVM Cost value, SVM Gamma value, precisions, sensitivity, specificity and accuracy of the selected models are presented for 37 kinase-specific groups with at least 20 experimentally verified phosphorylation sites. In the column of trained features, the value in the parentheses behind the coupling pattern (CP) is the value of difference or quotient of coupling strength between the training set against the background set. The average predictive accuracies of phosphoserine, phosphothreonine, phosphotyrosine and phosphohistidine are 90, 93, 88 and 93%, respectively.
