Fig. 4.

PPI network and machine learning. (A) PPI network. The number of lines between the two proteins represents the strength of the interaction. (B) 5 × CV Accuracy of SVM-RFE machine learning. (maximal accuracy = 0.86). The X axis is the number of features and the Y axis represents the accuracy of the curve change after 5 times cross-validation. 6–0.86 means that the accuracy rate of 6 features is 0.86. The closer the accuracy is to 1, the higher the accuracy. (C) 5 × CV Error of SVM-RFE machine learning. (minimal RMSE = 0.14). The X axis represents the number of features, and the Y axis represents the error rate of curve changes after 5 times cross-validation. 6–0.14 indicates that the error rate of 6 features is 0.14. The closer the accuracy is to 0, the lower the error rate. (D) Lasso machine learning. Lasso machine learning obtained 7 genes that contributed more to the model prediction through regression analysis and cross-validation. (E) Venn diagram of Lasso and SVM-RFE machine learning. PPI network protein–protein interaction network, LASSO least absolute shrinkage and selection operator, SVM-RFE support vector machine recursive feature elimination.