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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