Since the features of coupling patterns (CP ratio) and sequences (7-classes) with best predictive performance are combined, the average predictive accuracy of SVM models trained with the combined features of phosphoserine is 89%, which is slightly better than the SVM models trained only with coupling patterns. However, the average predictive performance of the SVM models trained with the combined features of phosphothreonine, phosphotyrosine and phosphohistidine is close to the SVM models trained only with coupling patterns. The overall predictive accuracy of SVM models trained with the combined features of coupling patterns and sequences is close to 91%. In addition, the method of KinasePhos 1.0 is evaluated based on the data set constructed in this work. The average predictive accuracies of phosphoserine, phosphothreonine, phosphotyrosine and phosphohistidine are 84, 88, 84 and 83%, respectively.
