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Table 2 The performance of OptNCMiner and baseline models with the base dataset and transfer learning dataset

From: OptNCMiner: a deep learning approach for the discovery of natural compounds modulating disease-specific multi-targets

Model Performance metric1 Base dataset Transfer learning dataset
OptNCMiner Recall 0.833 0.871
AUROC 0.632 0.787
Accuracy 0.440 0.713
Cosine similarity Recall 0.573 0.696
AUROC 0.643 0.761
Accuracy 0.708 0.818
Naïve bayes classifier Recall 0.322 0.483
AUROC 0.623 0.696
Accuracy 0.909 0.887
Logistic regression Recall 0.212 0.581
AUROC 0.606 0.785
Accuracy 0.978 0.969
Random forest Recall 0.677 0.479
AUROC 0.343 0.241
Accuracy 0.028 0.027
Multi-layer perceptron Recall 0.361 0.824
AUROC 0.676 0.818
Accuracy 0.972 0.899
  1. 1All performance metrics are weighted averages of the results of all proteins comprising the dataset