# Accurate Prediction and Key Feature Recognition of Immunoglobulin
## 3 Results and Discussion
### Independent Test Set Evaluation  
In order to evaluate the generalization ability of monoTriKGap and CC-PSSM models, we conducted two independent tests. Each test set had two tasks: one was to evaluate the generalization ability of monoTriKGap optimal feature subset for accurate prediction of immunoglobulin under SVM, and the other was to evaluate the generalization ability of CC-PSSM for recognition of key features of immunoglobulin under MLP.  
In the first group, 112 sequences from human and rat data were selected as the training set, and 116 sequences from mouse data were selected as the test set, including 33 immunoglobulins and 83 non immunoglobulins. The second group selected 112 human and rat sequences in the benchmark dataset as the training set. Thirty-three immunoglobulin sequences and thirty-three non immunoglobulin sequences from mouse were selected to form a test set. Details are shown in Table 7.  
The 212 optimal feature subsets generated by each group of monoTriKGap were trained under different classifiers, and the ACC value comparison of the test set is shown in Figure 6. Figure 5(a) shows that the first set accurately predicted 87.93% of immunoglobulins under SVM, which is higher than the accurate values of C4.5, KNN, and RF (72.14%, 77.58%, and 60.34%, respectively). Figure 5(b) shows that the second test set accurately predicted 87.88% of immunoglobulins under SVM, and the accuracy values higher than C4.5, KNN and RF were 77.27%, 69.69%, and 78.78%, respectively. Therefore, the two sets of data show that monoTriKGap does have a good generalization ability for the accurate prediction of immunoglobulin.  
In addition, 760 features extracted by each group of CC-PSSM were reduced in dimension by MRMD1.0 and MRMD2.0, and the identified mixed two-dimensional key features CC(E, R, 1) and CC(D, T, 2) were trained. The ACC values of the test set were compared and are shown in Figure 7. Figure 6(a) shows that in the first test set, the multilayer perceptron classifier correctly predicted 91.07% immunoglobulin, which is 0.9% higher than the C4.5 algorithm, 7.45% higher than the KNN algorithm, and 13.49% higher than the RF algorithm. Figure 6(b) shows that in the second test set, the multilayer perceptron classifier correctly predicted 90.90% of immunoglobulins, which is 3.03% higher than the C4.5 algorithm, 10.6% higher than the KNN algorithm, and 13.36% higher than the RF algorithm. Combined with the two groups of data, it can be concluded that CC-PSSM has a good generalization ability for key feature recognition. However, in order to ensure the prediction and recognition ability of the model for immunoglobulins, our future work will extend the data for further study.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Selection** & **Classifier** & **TPR** & **FPR** & **Precision** & **MCC** & **auROC** & **ACC** \\ \hline \multirow{3}{*}{CC–PSSM} & \multirow{3}{*}{MRMD} & MLP & 0.921 & 0.081 & 0.921 & 0.842 & 0.934 & 0.9211 \\  & & KNN & 0.895 & 0.106 & 0.895 & 0.789 & 0.928 & 0.8947 \\ \cline{1-1}  & 1.0 + 2.0 & C4.5 & 0.904 & 0.098 & 0.904 & 0.807 & 0.897 & 0.9035 \\ \cline{1-1}  & & RF & 0.886 & 0.114 & 0.886 & 0.771 & 0.930 & 0.8859 \\ \hline \hline \end{tabular}
\end{table}
Table 6: MRMD1.0+MRMD2.0 recognizes the comparison of the two–dimensional key features represented by CC–PSSM under other classifiers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Training Dataset**} & \multicolumn{3}{c}{**Independent Dataset**} \\ \cline{2-7}  & **Composition** & **Positive** & **Negative** & **Composition** & **Positive** & **Negative** \\ \hline Group1 & human, rat & 76 & 36 & mouse & 33 & 83 \\ Group2 & human, rat & 76 & 36 & mouse & 33 & 33 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Details for each group of independent test sets.