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. 2023 Jan 19;6:73. doi: 10.1038/s42003-023-04462-5

Fig. 2. Experimental results are presented to reveal the effectiveness of the model.

Fig. 2

a Radar chart of evaluation indicators corresponding to the different window sizes. b Showing the performance comparison of ProtT5, MBF, and combined features on the classifier, where the “Average evaluation metric values” refers to the average of the eight evaluation metrics (including TPR, TNR, Pre, ACC, F1, MCC, AUROC, and AP) for the different feature descriptors on these three datasets. c Demonstrating the performance comparison between the EDLMPPI architecture and 10 mainstream machine learning models and deep learning models: EDLMPPI is particularly strong in key metrics. d Performance comparison between different methods for imbalance dataset resolution, where the “Average evaluation metric values” refers to the average of the eight evaluation metrics (including TPR, TNR, Pre, ACC, F1, MCC, AUROC, and AP) for the different algorithms on these three datasets.