To further investigate the importance of the features and reveal the biological meaning of the features in PSSM-DT, we followed the study [50,70,71] to calculate the discriminant weight vector in the feature space. The sequence-specific weight obtained from the SVM training process can be used to calculate the discriminant weight of each feature to measure the importance of the features. Given the weight vectors of the training set with N samples obtained from the kernel-based training A = [a1, a2, a3,...,aN], the feature discriminant weight vector W in the feature space can be calculated by the following equation: