## 4 Experimental Results and Analysis
### Comparison with Other RBP Prediction Methods on Independent Validation Datasets
In the three deep-learning-based methods, our method CnnRBP performs slightly worse than iDRBP_MMC in the AUC and BACC by about 1%. However, CnnRBP achieves a significantly better performance than iDRBP_MMC in the other two overall indexes of the F1 measure and MCC with improvements of 4.9% and 6%. Meanwhile, CnnRBP significantly outperforms iDRBP_MMC in the ACC, PRE and SPE. It is worth noting that CnnRBP only makes use of statistical information directly from amino acid sequences, while iDRBP_MMC takes as input more complicated PSSMs obtained via sequence alignments. Another deep-learning-based method (i.e., Deep-RBPPred) performs poorly, maybe due to the feature representation being too simple. On the _E. coli_ dataset, our method performs best among all methods with significant improvements in the ACC and all overall indexes, such as the F1 measure, BACC, MCC and AUC. Compared with another deep model, iDRBP_MMC, our method achieves improvements of 6%, 6% and 8.08% in the AUC, MCC and BACC, respectively. It can be found that our method performs slightly worse than iDRBP_MCC in SPE but achieves a significantly better performance in SEN, which shows that our method has advantages over iDRBP_MMC in identifying RBPs. On this dataset, the shallow method TriPepSVM performs similarly or even better than iDRBP_MMC, which shows the effectiveness of short motifs as protein representation for RBP detection. On the Salmonella dataset, our method performs best in almost all indexes. Specifically, our method achieves an AUC of 0.91, MCC of 0.73, F1 measure of 77.19% and BACC of 84.08%, respectively. Compared with iDRBP_MMC, our method makes improvements in all indexes. Our method outperforms iDRBP_MMC with improvements of 12.78%, 6.6%, 15% and 1% in the F1 measure, BACC, MCC, and AUC, respectively. Compared with TriPepSVM, our method achieves significant improvements in the F1 measure, BACC, MCC and AUC by 10.53%, 7.72%, 10% and 5%, respectively. These results show that the prediction performance of our method is better than other existing methods with the help of the deep learning framework. To compare the performances of different methods more intuitively, the ROC curves and PR curves are drawn in Figures 5-7 for the compared methods on the independent validation datasets of the three species. As shown in Figure 5, the two deep models, i.e., CnnRBP and iDRBP_MMC, are close to each other for both the ROC curves and PR curves on the human dataset, significantly outperforming other shallow models. Furthermore, based on the same features, the proposed CnnRBP is superior to TriPepSVM, which shows that the deep model can achieve a better performance than the shallow model. Deep-RBPPred and the other three shallow models show poor performances on this dataset, probably due to weak feature representations. As shown in Figures 6 and 7, a similar conclusion to the one above can be found, which is that the deep models outperform the shallow models. Specifically, our method outperforms another deep model (i.e., iDRBP_MMC) significantly on these two datasets, indicating the advantages of species-specific models. Our method trained species-specific models for different species, which took into account the diversity of species. In conclusion, benefiting from the strong representation ability of deep learning, our method achieved a significantly greater performance improvement compared to the shallow models. Meanwhile, our method was quite competitive when compared to the other deep model, despite using simpler features. Figure 5: Comparison of AUPR and AUC values of the proposed CnnRBP with other methods on human independent validation dataset. Figure 6: Comparison of AUPR and AUC values of the proposed CnnRBP with other methods on _E. coli_ independent validation dataset. Figure 7: Comparison of AUPR and AUC values of the proposed CnnRBP with other methods on Salmonella independent validation dataset.