## 3 Results
### Performance of PPI Prediction
#### 3.4.1 Performance of the Proposed Method with Three Diverse PPI Datasets  
In our problem of PPI predictions, the dimension of input features is 2400, which may contain the unimportant information and noise. Thus, the PCA algorithm is used to eliminate noise in the dataset. However, it is hard to determine the optimal number of features to use. Here, we carried out the experiments to find the optimal PCA dimension. For the sake of example, for the _H. pylori_ PPI dataset, the prediction performance of different PCA dimensions is shown in Table 1. As a result, the favorable number of PCA dimensions is 1000-dimensional.  
For the sake of avoiding over-fitting and of verifying the constancy of the model, 5-fold cross-validations were used, which is part of the sub-sampling test method. More specifically, in 5-fold cross-validation, the entire dataset is split into 5 parts, where 4 parts are applied as training samples and 1 part is used as testing samples. In this way, we obtain five models from the datasets, and each model is a separate experiment. The prediction results of three datasets with the RP ensemble classifier are based on protein sequences and evolutionary information shown in Tables 2-4.  
As shown in Tables 2-4, when predicting the Yeast PPI dataset by applying our proposed method, we gained the average _Acc_, _PE_, _Sen_, and _MCC_ of 95.64%, 96.75%, 94.47% and 91.30%, respectively. The standard deviations of _Acc_, _PE_, _Sen_, and _MCC_ are 0.52%, 0.45%, 0.47% and 1.03%, respectively. When predicting the Human PPI dataset, good results are obtained: the average _Acc_, _PE_, _Sen_, and _MCC_ are 96.59%, 96.18%, 96.72%, and 93.18%, respectively. The standard deviations are 1.24%, 1.17%, 1.41% and 2.49%, respectively. When predicting the _H. pylori_ PPI dataset, the average _Acc_, _PE_, _Sen_, and _MCC_ are 87.62%, 99.20%, 75.82% and 77.40%, with the corresponding standard deviations of 1.10%, 0.36%, 2.47% and 1.92%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Dimension** & **600-D (\%)** & **700-D (\%)** & **800-D (\%)** & **900-D (\%)** & **1000-D (\%)** & **1100-D (\%)** & **1200-D (\%)** & **1300-D (\%)** & **1400-D (\%)** \\ \hline Accuracy & 81.82 & 68.78 & 78.90 & 68.27 & 88.51 & 84.91 & 79.25 & 71.36 & 85.59 \\ Sensitivity & 63.67 & 80.95 & 84.42 & 62.04 & 77.82 & 71.10 & 60.98 & 80.07 & 72.05 \\ Specificity & 98.36 & 58.06 & 73.94 & 73.97 & 99.31 & 99.65 & 96.96 & 62.06 & 99.65 \\ Precision & 97.25 & 62.96 & 74.44 & 67.73 & 99.13 & 99.53 & 95.11 & 69.25 & 99.53 \\ MCC & 66.86 & 39.78 & 58.44 & 36.12 & 78.90 & 73.27 & 62.32 & 42.91 & 74.30 \\ \hline \end{tabular}
\end{table}
Table 1: The prediction performance of different principal component analysis (PCA) dimensions on the _H. pylori_ protein–protein interaction (PPI) dataset.  
The Receiver Operating Characteristic (ROC) curves for Yeast, Human, and \(H\). _pylori_ PPI datasets with five-fold cross-validation are shown in Figures 1-3, respectively. We computed the average AUC (area under ROC curve) values of Yeast, Human, and \(H\). _pylori_ PPI datasets to be 0.9570, 0.9615, and 0.8287, respectively. In conclusion, the higher accuracies and lower standard deviations of these values indicate that our presented approach is feasible and reasonable for detecting PPIs.  
Compared with the Human and the Yeast PPI datasets, the prediction performance of the _H. pylori_ dataset is lower. It should be noticed that the sample sizes of the Human, Yeast, and _H. pylori_ datasets are 8161, 11,188, and 2916, respectively. We found that the prediction performance, as indicated by the accuracy score, improves as the size of the samples increases.