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Table 3 Comparison of performance indicators to several DILI classification models reported in the literature

From: Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure

Model algorithm

Number of Compounds

CV scheme

CV Balanced accuracy 

CV Sensitivity

External test set Balanced accuracy

External test set Sensitivity

References

RF

996 (541+/455-)

10-fold, random splits

0.645

0.680

0.588

0.536

Kotsampasakou et al. (2017) [9]

SVM

1317 (571+/407-)

5-fold, splitting scheme unknown

0.767

0.948

0.597

0.848

Zhang et al. (2016) [10]

Ensemble of RF and SVM models (5 total)

1241 (683+/558-)

5-fold, splitting scheme unknown

0.701

0.799

0.719

0.909

Ai et al. (2018) [7]

Ensemble of eight different algorithms derived models (8 total)

1254 (636+/618-)

10-fold, splitting scheme unknown

0.783

0.818

0.716

0.773

He et al. (2019) [8]

SVM

401 (174+/227-)

5-fold, Tanimoto similarity based GroupKFold

0.714 ± 0.06

0.697 ± 0.08

0.759 ± 0.03

0.724 ± 0.08

Present study

  1. Literature model performance derived from He et al. (2019) [8]. External test values quoted for the model developed in the present study are for the external test set. Despite being trained on the fewest compounds (401) and using a conservative LOCO-CV cross-validation scheme, the SVM model developed in the present study demonstrated robust predictivity between cross-validation and external test set