Accurate Prediction and Key Feature Recognition of Immunoglobulin
Immunoglobulin, which is also called an antibody, is a type of serum protein produced by B cells that can specifically bind to the corresponding antigen. Immunoglobulin is closely related to many diseases and plays a key role in medical and biological circles. Therefore, the use of effective methods to improve the accuracy of immunoglobulin classification is of great significance for disease research. In this paper, the CC–PSSM and monoTriKGap methods were selected to extract the immunoglobulin features, MRMD1.0 and MRMD2.0 were used to reduce the feature dimension, and the effect of discriminating the two–dimensional key features identified by the single dimension reduction method from the mixed two–dimensional key features was used to distinguish the immunoglobulins. The data results indicated that monoTrikGap (k = 1) can accurately predict 99.5614% of immunoglobulins under 5-fold cross–validation. In addition, CC–PSSM is the best method for identifying mixed two–dimensional key features and can distinguish 92.1053% of immunoglobulins. The above proves that the method used in this paper is reliable for predicting immunoglobulin and identifying key features.