# Accurate Prediction and Key Feature Recognition of Immunoglobulin
## 3 Results and Discussion
### Key Feature Analysis  
This study introduced the use of MRMD1.0 and MRMD2.0 for two-dimensional key feature recognition. Key feature recognition and analysis were performed on the feature subsets extracted based on CC-PSSM and monoTriKGap, respectively. First, based on the 760 features extracted by CC-PSSM, MRMD1.0 was used to reduce the dimensionality, and the first two features were selected according to the ranking information to be CC(P, D, 2) and CC(E, R, 1) as the first group two-dimensional key features. Second, MRMD2.0 was used to reduce the dimensionality of the original feature set; the number of generated features was set to two, and the two matched features were CC(D, T, 2) and CC(H, C, 1) as the second group of two-dimensional key features. Then, the key features of the first two groups were combined in any two pairs to generate four sets of 2-dimensional feature combinations that were different from the first two groups. The feature combined with the largest classification index ACC value, namely CC(E, R, 1) and CC(D, T, 2), were selected as the two-dimensional key features of MRMD1.0 and MRMD2.0. Based on the 212 best features extracted by monoTriKGap, the above steps were also performed. The first two-dimensional key features, including D_DDD and H_HHD, were obtained through MRMD1.0. Through MRMD2.0, the second set of two-dimensional key features, including F_HHV and D_HHF, were obtained. By comparing the ACC values of four sets of two-dimensional hybrid features in any combination, we obtained the hybrid two-dimensional key features of MRMD1.0 and MRMD2.0, including H_HHD and F_HHV. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Kernel Function** & **TPR** & **FPR** & **Precision** & **MCC** & **auROC** & **ACC** \\ \hline liner kernel & 0.996 & 0.004 & 0.996 & 0.991 & 0.996 & 0.9956 \\ polynomial kernel & 0.864 & 0.138 & 0.864 & 0.728 & 0.863 & 0.8640 \\ RBF & 0.746 & 0.277 & 0.821 & 0.554 & 0.734 & 0.7456 \\ \hline \end{tabular}
\end{table} 
Table 3: The prediction results of the best feature subset of monoTriKGap under different kernel functions of support vector machine. To analyse the ability of each group of key features to distinguish immunoglobulin, the classification performance of the three groups of key features of CC-PSSM and monoTriKGap was evaluated. Three classifiers of Naive Bayes, SVM, and multilayer perceptron classifiers were used under 5-fold cross validation. The results of the two-dimensional key feature analysis are shown in Table 4.  
The research results in Table 4 show that after the features extracted by CC-PSSM reduce the dimensionality, the mixed two-dimensional key features of MRMD1.0 and MRMD2.0 have better classification performance than the single-obtained two-dimensional key features. The ACC value of mixed two-dimensional key features using the multilayer perceptron classifier was 92.1053% higher than the ACC value of the single group of two-dimensional key features, which were 90.3509% and 84.6491%, respectively. At this time, the TPR was 0.921, the FPR was 0.081, the MCC was 0.842, and the ROC curve area was 0.934. Similarly, after the features extracted by monoTriKGap reduced the dimensionality, the classification performance of the mixed two-dimensional key features was also better than the classification performance of a single group of two-dimensional key features. At this time, MRMD1.0 and MRMD2.0 mixed two-dimensional key features using a Naive Bayes classifier to achieve the ACC value of 53.9474%. Most importantly, the mixed two-dimensional key features of the CC-PSSM feature extraction method were better than monoTriKGap, which proves that the CC-PSSM feature extraction method could better identify the key features to distinguish immunoglobulins. Figure 5 shows the scatter plot of the 2-dimensional mixed features recognized by CC-PSSM to distinguish immunoglobulins from non immunoglobulins.