Table 3.

Performance comparison with states-of-the-art models under test pattern 1

Test Set	Model	auROC	auPRC	Pearson value	Spearman value
Total test set	CnnCrispr	0.975	0.679	0.682	0.154
	CFD	0.942	0.316	0.343	0.140
	MIT	0.77	0.044	0.150	0.085
	CNN_std	0.947	0.208	0.321	0.141
	DeepCrispr	0.981	0.497	–	0.133
Hek293t test set	CnnCrispr	0.971	0.686	0.712	0.160
	CFD	0.936	0.318	0.371	0.143
	MIT	0.756	0.048	0.153	0.084
	CNN_std	0.939	0.204	0.330	0.144
	DeepCrispr	0.984	0.521	–	0.136
K562 test set	CnnCrispr	0.995	0.688	0.426	0.134
	CFD	0.965	0.322	0.336	0.128
	MIT	0.814	0.033	0.057	0.086
	CNN_std	0.983	0.287	0.319	0.132
	DeepCrispr	0.953	0.41	–	0.126

We downloaded the prediction models of CFD, MIT and CNN_std from relevant websites and obtained the prediction results on the same test set as CnnCrispr. Since the training process of CnnCrispr was consistent with DeepCrispr’s, we directly used the test results in Additional file 2 given by DeepCrispr for performance comparison. The numbers in boldface indicate the highest scores for each metric