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Table 7 Comparison with embedding-based methods

From: A multitask transfer learning framework for the prediction of virus-human protein–protein interactions

Dataset Model AUC AP Precision Recall F1
Zhou’s H1N1 doc2vec 0.9601 0.9674 89.04 89.34 89.19
MotifTransformer 0.945 86.50
MTT 0.9461 0.9589 86.28 86.51 86.40
Zhou’s Ebola Doc2vec 0.9781 0.9832 91.99 92.67 92.33
MotifTransformer 0.968 89.6
MTT 0.9680 0.9766 90.93 91.53 91.23
Denovo_slim doc2vec 0.9644 0.9681 88.60 88.87 88.73
MTT 0.9221 0.9324 83.92 84.12 84.02
Barman doc2vec 0.8671 0.8922 79.95 80.37 80.16
MTT 0.9804 0.9802 93.53 94.05 93.79
Bacillus doc2vec 0.9900 0.9739 96.29 96.32 96.31
MTT 0.9997 0.9992 98.75 98.78 98.76
Yersina doc2vec 0.9814 0.9510 94.50 94.52 94.51
MTT 0.9988 0.9971 97.32 97.34 97.32
Franci doc2vec 0.9878 0.9606 96.77 96.84 96.81
MTT 0.9998 0.9996 98.95 99.03 98.99
  1. The bold font is used to highlight highest scores corresponding to each dataset