Table 5.

The comparison of different methods for the average evaluation values in all cells

Dataset	Methods	Acc(%)	AUC	Fscore(%)	MCC(%)	SP	SE(%)
HCCs	LightCpG	92.06	0.9616	84.66	78.97	93.73	86.84
	DeepCpG	92.34	0.9689	86.42	81.24	92.95	90.59
	RF Zhang	88.41	0.9351	79.93	72.08	89.38	85.59
HepG2	LightCpG	83.20	0.9213	81.73	67.36	89.96	78.32
	DeepCpG	84.17	0.9248	82.52	68.22	85.27	83.40
	RF Zhang	81.16	0.8942	80.17	63.20	87.39	76.29

¹LightCpG employs three types of features (sequence feature, structural feature and positional feature) and LightGBM [36] to identify the CpG sites

²DeepCpG [35] embodies the connection between various cells by using the deep learning model Gated Recurrent Network (GRU) and also extracts features from the DNA sequence by the convolutional neural network (CNN) and one additional fully connected hidden layer. Then DeepCpG uses Fully Connected Neural Network to identify CpG sites

³RF Zhang [1] extracts the genomic positional features, neighbor features, sequence properties and sic-regulatory elements to identify the CpG sites

⁴The boldface is the best value in the column