Table 2. Accuracy and AUC value of journal publications based on different datasets and models.

Author	Data type	Models	Accuracy (%)	AUC
Wang et al. (13)	Mutation features	Extreme learning machines	79.17	NA
	Mutation features; pathological features; demographic features		95.83
Emaminejad et al. (14)	Genomic features	Multilayer perceptron; Naïve Bayes	NA	0.68
	Radiomic features			0.78
	Integrated dataset			0.84
Yu et al. (15)	Genomic features; transcriptomics/proteomics features; histopathology features	Random forest	NA	0.81
Matsubara et al. (17)	PPI network; gene expression	Convolutional networks	83.16	NA
		Radom forest	82.63
		Support vector machines	81.58
Malik et al. (18)	Copy number variations; mutation; protein; RNA; mi-RNA	Support vector machines	72.7	NA
		Neural network	92.9
		RUS ensemble boost	66.7
Giang et al. (20)	Gene expression	Support vector machines	62.50	0.6964
	DNA methylation		71.88	0.6235
	mi-RNA expression		65.63	0.722
	Integrated dataset		78.13	0.7227

AUC, area under the curve; RUS, random undersampling; NA, not available; PPI, protein-protein interaction.