Protein Sequence Comparison and DNA-binding Protein Identification with Generalized PseAAC and Graphical Representation

Li, Chun; Zhao, Jialing; Wang, Changzhong; Yao, Yuhua

In order to show the advantage of their method, Xu et al. [4] created a dataset called expanded benchmark dataset1100 with all the 146 positive samples and 1100 negative samples in DNAeSet, which is employed as another training dataset to evaluate the predictive performance on the independent dataset DNAiSet. For convenience of comparison, we also select the expanded benchmark dataset to establish the classifier and test it on DNAiSet. Repeating this procedure five times, the average results are given in Table 7 (the first row). Results obtained by the other four methods (DNAbinder, DNA-Prot, iDNA-Prot and enDNA-Prot) trained on the expanded benchmark dataset with n=1100 are also listed in Table 7 . From this table we see that the overall accuracy of our method is about 92%, with MCC of 0.84 and F1M of 91.24%, which outperforms other methods with improvement in the range of 2.49-19.12% in terms of ACC, 0.05-0.32 in terms of MCC, and 3.82-33.85% in terms of F1M. This suggests that our method performs well on unbalanced datasets.