Prediction for Membrane Protein Types Based on Effective Fusion Representation and MIC-GA Feature Selection

Membrane proteins occupy an important position in the life activities of humans and other species. The elucidation of membrane protein types provides clues for understanding the structure and function of proteins. With the fusion of various protein information including amino acid classification, physicochemical property, and evolutionary information, this paper proposes a system for predicting membrane protein types. In this system, a new feature selection method called MIC-GA is proposed to deal with the curse of high-dimensional features. The findings show that this approach is effective in reducing feature dimensions and improves prediction accuracy. Ensemble method based on stacked generalization is also used to solve the problem of feature heterogeneity. The performance of the present method is evaluated on two benchmark datasets. The overall prediction accuracies of eight types are 89.23% and 93.49% using jackknife test and independent test, respectively. The final experimental results show that our method is more effective than the existing methods for prediction of membrane protein types.