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Table 3 AUPR scores (mean and standard deviation obtained by nested 5-fold cross-validation) on the DBEColi dataset in the four \(S_1\), \(S_2\), \(S_3\), or \(S_4\) settings, and for a test sample positive:negative ratio 1:5

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

 

\(S_1\)

\(S_2\)

\(S_3\)

\(S_4\)

kronSVM

\(\mathit{61.55} \pm \mathit{3.11}\)

\(\mathit{28.95} \pm \mathit{7.66}\)

\(\mathit{60.96} \pm \mathit{3.73}\)

\(24.4 \pm 8.8\)

NRLMF

\(\mathit{59.89} \pm \mathit{4.35}\)

\(\mathit{35.62} \pm \mathit{8.07}\)

\(\mathit{60.06} \pm \mathit{2.73}\)

\(\mathit{34.5} \pm \mathit{9.75}\)

FNN

\(51.55 \pm 2.55\)

\(24.26 \pm 3.91\)

\(49.34 \pm 3.11\)

\(22.97 \pm 6.59\)

Chemogenomic neural network (CN)

\(49.08 \pm 4.31\)

\(\mathit{28.38} \pm \mathit{5.81}\)

\(46.14 \pm 4.92\)

\(\mathit{27.0} \pm \mathit{6.96}\)

RF with proteochemometric features

\(53.49 \pm 2.19\)

\(23.6 \pm 4.13\)

\(51.16 \pm 4.32\)

\(22.51 \pm 5.36\)