BMC Bioinformatics

Table 2 Results from implemented models when tested on multiple benchmark datasets

From: Supervised promoter recognition: a benchmark framework

Model	hg38 dataset
	4,602,408 non-promoter sequences
	Sn		Sp		PPV		MCC
CNNProm	0.584		0.943		0.014		0.0009
ICNNP	0.474		0.944		0.012		0.0007
DProm	0.931		0.025		0.001		− 0.0001

Model	hg38chr1 dataset				hg38chr2 dataset
	192,476 non-promoter sequences				224,056 non-promoter sequences
	Sn	Sp	PPV	MCC	Sn	Sp	PPV	MCC
ICNNP	0.470	0.890	0.077	0.154	0.487	0.908	0.058	0.143
DProm	0.891	0.024	0.017	− 0.073	0.887	0.022	0.011	− 0.066

Model	mm10chr1 dataset				mm10chr2 dataset
	191,094 non-promoter sequences				193,183 non-promoter sequences
	Sn	Sp	PPV	MCC	Sn	Sp	PPV	MCC
ICNNP	0.342	0.904	0.058	0.106	0.369	0.895	0.070	0.120
DProm	0.867	0.046	0.15	− 0.053	0.856	0.050	0.019	− 0.060

Models are clearly lacking in precision (PPV)

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