Table 1 Overview of the performance of the benchmarked methods expressed as z-scores per experiment

From: Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set

Method	MCC random	BEDROC random	MCC temporal	BEDROC temporal	Average	SEM
NB 10 μM	−2.41	−2.22	−2.07	−0.67	−1.84	0.40
NB	−0.65	−0.66	−0.81	−0.64	−0.69	0.04
RF	0.56	−0.30	0.02	−1.41	−0.28	0.41
RF_PCM	0.88	−0.17	−0.46	−1.10	−0.21	0.41
SVM	0.11	0.36	0.53	0.30	0.32	0.09
LR	0.17	0.40	0.11	0.19	0.22	0.06
DNN	0.32	0.75	0.56	0.79	0.60	0.11
DNN_MC	0.60	0.85	1.03	1.20	0.92	0.13
DNN_PCM	0.44	0.98	1.09	1.33	0.96	0.19

Z-scores are shown for all methods for both types of splitting and for both MCC and BEDROC. In italics the best performance for a given machine learning algorithm per column is highlighted. See main text for further details

Back to article page