# Accurate Prediction and Key Feature Recognition of Immunoglobulin
## 4 Conclusions  
The main work of protein prediction consists of two steps: one step is the selection of the feature representation method, and the other step is to reduce the feature dimension and identify key features. As a significant component of the immune system, immunoglobulin is closely related to various diseases. Accurate prediction of immunoglobulin can be more beneficial to drug development and disease treatment. This research focuses on the accurate prediction of immunoglobulin and the recognition of key features. By comparing the feature representation methods of CC-PSSM and monoTriKGap, the best feature set generated by monoTriKGap through the AdaBoost classification model is found to be able to accurately predict 99.5614% of immunoglobulins under the SVM classifier. For the identification of key features, unlike the past, we considered MRMD1.0 and MRMD2.0 for key feature screening and consider two-dimensional hybrid key features. The results show that the features extracted by CC-PSSM are identified by the mixed two-dimensional key, and 92.1053% of immunoglobulins can be distinguished under the multilayer perceptron classifier. Therefore, the method used in this article can be used as a powerful means to study immunoglobulin. In future work, we will collect and expand the dataset, and use more data to verify the effectiveness of the model. In order to improve the performance of the SVM algorithm, some important parameters of the algorithm (such as penalty coefficient C) will be optimized. At the same time, to avoid overfitting, we will consider adding related regularization tests in our future work.  
Figure 6: Two independent test set used the monoTriKGap feature extraction method to predict the performance evaluation of immunoglobulin under different classifiers (**a,b**).  
Figure 7: The performance evaluation of two independent test sets through the CC–PSSM feature extraction method to identify key features under different classifiers (**a,b**).  
**Author Contributions:** Conceptualization, B.L. and Q.Z.; data collection or analysis, Y.G. and D.P.; validation, Y.G.; writing--original draft preparation, Y.G.; writing--review and editing, Y.G. and Q.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Nature Science Foundation of China (Grant Nos 61863010, 11926205, 11926412, and 61873076), National Key R & D Program of China (No.2020 YFB2104400), Natural Science Foundation of Hainan, China (Grant Nos. 119 MS036 and 120 RC588), Hainan Normal University 2020 Graduate Student Innovation Research Project (hsyx2020-40) and the Special Science Foundation of Quzhou (2020D003).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data and available code for this study are freely available for download on [[https://github.com/gongxiaodou/Immunoglobulin](https://github.com/gongxiaodou/Immunoglobulin)]([https://github.com/gongxiaodou/Immunoglobulin](https://github.com/gongxiaodou/Immunoglobulin)) (accessed on 21 July 2021).  
**Conflicts of Interest:** The authors declare no conflict of interest.