The evaluation results of MIDTI with different similarity network fusion strategy on Luo’s, and Zheng’s datasets.
| Datasets . | Strategy . | ACC . | AUC . | AUPR . |
|---|---|---|---|---|
| MIDTI_ave | 0.9078 | 0.9547 | 0.9336 | |
| Luo | MIDTI_pro | 0.8961 | 0.9611 | 0.9501 |
| MIDTI | 0.9340 | 0.9787 | 0.9701 | |
| MIDTI_ave | 0.8162 | 0.8789 | 0.8665 | |
| Zheng | MIDTI_pro | 0.8048 | 0.8803 | 0.8714 |
| MIDTI | 0.8836 | 0.9546 | 0.9497 |
| Datasets . | Strategy . | ACC . | AUC . | AUPR . |
|---|---|---|---|---|
| MIDTI_ave | 0.9078 | 0.9547 | 0.9336 | |
| Luo | MIDTI_pro | 0.8961 | 0.9611 | 0.9501 |
| MIDTI | 0.9340 | 0.9787 | 0.9701 | |
| MIDTI_ave | 0.8162 | 0.8789 | 0.8665 | |
| Zheng | MIDTI_pro | 0.8048 | 0.8803 | 0.8714 |
| MIDTI | 0.8836 | 0.9546 | 0.9497 |
The best results are in bold and the second best results are underlined.
The evaluation results of MIDTI with different similarity network fusion strategy on Luo’s, and Zheng’s datasets.
| Datasets . | Strategy . | ACC . | AUC . | AUPR . |
|---|---|---|---|---|
| MIDTI_ave | 0.9078 | 0.9547 | 0.9336 | |
| Luo | MIDTI_pro | 0.8961 | 0.9611 | 0.9501 |
| MIDTI | 0.9340 | 0.9787 | 0.9701 | |
| MIDTI_ave | 0.8162 | 0.8789 | 0.8665 | |
| Zheng | MIDTI_pro | 0.8048 | 0.8803 | 0.8714 |
| MIDTI | 0.8836 | 0.9546 | 0.9497 |
| Datasets . | Strategy . | ACC . | AUC . | AUPR . |
|---|---|---|---|---|
| MIDTI_ave | 0.9078 | 0.9547 | 0.9336 | |
| Luo | MIDTI_pro | 0.8961 | 0.9611 | 0.9501 |
| MIDTI | 0.9340 | 0.9787 | 0.9701 | |
| MIDTI_ave | 0.8162 | 0.8789 | 0.8665 | |
| Zheng | MIDTI_pro | 0.8048 | 0.8803 | 0.8714 |
| MIDTI | 0.8836 | 0.9546 | 0.9497 |
The best results are in bold and the second best results are underlined.
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