A novel riboswitch classification based on imbalanced sequences achieved by machine learning
Fig 4
The figures showed a comparison of the balanced and imbalanced sequences and performance of classifiers.
It has been done using the Wilcoxon rank test, A) Accuracy showed significant difference between balanced and imbalanced sequences (p < 0.05) C) Sensitivity showed very significant difference between balanced and imbalanced sequences (p < 0.001) E) Specificity revealed no significant differences at all levels G) F-score showed very significant difference between balanced and imbalanced sequences (p < 0.001). Classifiers performance evaluation on imbalanced and imbalanced sequences shown as B) Accuracy resulted to have significant difference in all classifiers except KNN (p < 0.05, p < 0.01, p < 0.001) D) Sensitivity observed to have significant difference in only MLP and SVM (p < 0.05) whereas the remaining algorithms showed no differences F) Specificity depicted significant differences in NB, SVM and KNN (p < 0.05) on the other hand MLP, RF and GB showed no differences in both sequences group H) F-score depicted very significance differences in NB (p < 0.01), RF (p < 0.001) and SVM (p < 0.001) whereas KNN and MLP showed no differences. Violin box was used to depict the statistical differences between two group were provided as the plots. (* indicated significant difference of p < 0.05, ** denoted very significant difference of p < 0.01, and *** showed very significant difference p < 0.001).