## 4 Conclusions  
In this work, we proposed a computational method for identifying piRNAs with the function of instructing target mRNA deadenylation and piRNAs without the function of instructing target mRNA deadenylation. According to the average outcomes of 5-fold cross-validation test for running 10 times, the combination of BPB(15) + SNC(4) + DNC(8) + TNC(56) + PP(84) achieved the best _Sn_, _Sp_, _Acc_, and _MCC_ values for the first layer. While for the second layer, it was a bit complex. The original combination BPB(15) + SNC(4) + DNC(10) + TNC(48) + PP(24) must contain KNN (\(k\) = 10) features in N-terminal and C-terminal to get satisfactory _Sn_, _Sp_, _Acc_, and _MCC_ average results of 5-fold cross-validation test for 10 times. Moreover, the comparison between the jackknife and 5-fold cross-validation outcomes shows the robustness of 2L-piRNAPred. It should be pointed that the predictor was not tested on an independent dataset, and the prediction performance might cause a certain overfitting.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/8/11/2204/s1](http://www.mdpi.com/2076-3417/8/11/2204/s1)]([http://www.mdpi.com/2076-3417/8/11/2204/s1](http://www.mdpi.com/2076-3417/8/11/2204/s1)), Table S1: The average 5-fold cross-validation results using split-BPB on different window sizes both on N- and C- terminal for the first layer, Table S2: Features selection process for SNC by F-scores for the first layer, Table S3: Features selection process for DNC by F-scores for the first layer, Table S4: Features selection process for TNC by F-scores for the first layer, Table S5: Features selection process for physicochemical properties by F-scores for the first layer, Table S6: The average 5-fold cross-validation results using split-BPB on different window sizes both on N- and C- terminal for the second layer, Table S7: Features selection process for SNC by F-scores for the second layer, Table S9: Features selection process for TNC by F-scores for the second layer, Table S10: Features selection process for physicochemical properties by F-scores for the second layer.  
**Author Contributions:** [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] implemented SVM and created the webserver; [PERSON] performed the analysis and wrote the paper; [PERSON] and [PERSON] revised the paper. All authors read and approved the final manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (grant No. 71271034), the National Social Science Foundation of China (grant No. 15 CGL031), the Fundamental Research Funds for the Central Universities (grant No. 3132016306, 3132018160, 3132018227), the Program for Dalian High-Level Talent Innovation Support (grant No. 2015R063), the National Natural Science Foundation of Liaoning Province (grant No. 20180550307 and 20180550223), and the National Scholarship Fund of China for Studying Abroad.  
**Acknowledgments:** We are deeply grateful to the anonymous referees and to the editors for their suggestion and help in significantly improving the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & _Sn (\%)_ & _Sp (\%)_ & _Acc (\%)_ & _MCC_ \\ \hline
**First Layer** & & & & \\ SVM & 90.4 & 87.5 & 89 & 0.779 \\ RF & 85.8 & 88.4 & 87.1 & 0.743 \\ KNN & 88.7 & 83.6 & 86.1 & 0.724 \\ Ensemble & 89.9 & 87.0 & 88.5 & 0.770 \\ \hline
**Second Layer** & & & & \\ SVM & 84.3 & 83.6 & 84.0 & 0.680 \\ RF & 72.9 & 72.8 & 72.9 & 0.457 \\ KNN & 73.3 & 69.7 & 71.5 & 0.431 \\ Ensemble & 75.9 & 73.6 & 74.8 & 0.495 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performances of different classifiers on the jackknife test.