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Table 1 AUPR score for the two transfer learning modifications of the chemogenomic neural network CN, based on a single train/validation/test split of the DBEColi dataset

From: Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity

 Raw (\(S_1\))Orphan proteins (\(S_2\))Orphan molecules (\(S_3\))Double orphan (\(S_4\))
Chemogenomic neural network (CN)\(39.57 \pm 4.17\)\(26.74 \pm 2.49\)\(43.74 \pm 2.35\)\(\mathit{24.63} \pm \mathit{1.89}\)
Curriculum learning\(45.06 \pm 2.64\)\(21.43 \pm 3.62\)\(\mathit{51.45} \pm \mathit{3.03}\)\(20.97 \pm 2.70\)
\(CN-feaMLP\)\(\mathit{50.616} \pm \mathit{2.71}\)\(\mathit{30.89} \pm \mathit{4.93}\)\(42.04 \pm 2.95\)\(\mathit{25.77} \pm \mathit{3.60}\)
  1. The curriculum learning line corresponds to pre-training the molecule encoder of CN on the DBHuman dataset. The standard deviations are obtained by repeating 5 times the evaluation procedure