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Table 1

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Prediction performance in binary classification (Cluster-L, Signal-L)

	Scenario	NN	RF	CNN	Lasso	Enet	MDeep
Sensitivity	S1	0.6620	0.6534	0.6932	0.7208	0.7290	0.8188
	S2	0.6794	0.6730	0.6932	0.7480	0.7460	0.7904
Specificity	S1	0.6692	0.6586	0.6962	0.7346	0.7350	0.8254
	S2	0.6582	0.6508	0.6958	0.7432	0.7442	0.7720
Accuracy	S1	0.6656	0.6560	0.6947	0.7277	0.7320	0.8221
	S2	0.6688	0.6619	0.6945	0.7456	0.7451	0.7812
Precision	S1	0.6709	0.6594	0.6976	0.7387	0.7406	0.8271
	S2	0.6702	0.6593	0.6951	0.7437	0.7455	0.7777
MCC	S1	0.3354	0.3139	0.3914	0.4588	0.4665	0.6468
	S2	0.3420	0.3250	0.3909	0.4935	0.4921	0.5644
F1 score	S1	0.6622	0.6544	0.6933	0.7251	0.7310	0.8210
	S2	0.6712	0.6649	0.6923	0.7429	0.7443	0.7827

	Scenario	NN	RF	CNN	Lasso	Enet	MDeep
Sensitivity	S1	0.6620	0.6534	0.6932	0.7208	0.7290	0.8188
	S2	0.6794	0.6730	0.6932	0.7480	0.7460	0.7904
Specificity	S1	0.6692	0.6586	0.6962	0.7346	0.7350	0.8254
	S2	0.6582	0.6508	0.6958	0.7432	0.7442	0.7720
Accuracy	S1	0.6656	0.6560	0.6947	0.7277	0.7320	0.8221
	S2	0.6688	0.6619	0.6945	0.7456	0.7451	0.7812
Precision	S1	0.6709	0.6594	0.6976	0.7387	0.7406	0.8271
	S2	0.6702	0.6593	0.6951	0.7437	0.7455	0.7777
MCC	S1	0.3354	0.3139	0.3914	0.4588	0.4665	0.6468
	S2	0.3420	0.3250	0.3909	0.4935	0.4921	0.5644
F1 score	S1	0.6622	0.6544	0.6933	0.7251	0.7310	0.8210
	S2	0.6712	0.6649	0.6923	0.7429	0.7443	0.7827

Top performed method in each metric is bold.

Table 1

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Prediction performance in binary classification (Cluster-L, Signal-L)

	Scenario	NN	RF	CNN	Lasso	Enet	MDeep
Sensitivity	S1	0.6620	0.6534	0.6932	0.7208	0.7290	0.8188
	S2	0.6794	0.6730	0.6932	0.7480	0.7460	0.7904
Specificity	S1	0.6692	0.6586	0.6962	0.7346	0.7350	0.8254
	S2	0.6582	0.6508	0.6958	0.7432	0.7442	0.7720
Accuracy	S1	0.6656	0.6560	0.6947	0.7277	0.7320	0.8221
	S2	0.6688	0.6619	0.6945	0.7456	0.7451	0.7812
Precision	S1	0.6709	0.6594	0.6976	0.7387	0.7406	0.8271
	S2	0.6702	0.6593	0.6951	0.7437	0.7455	0.7777
MCC	S1	0.3354	0.3139	0.3914	0.4588	0.4665	0.6468
	S2	0.3420	0.3250	0.3909	0.4935	0.4921	0.5644
F1 score	S1	0.6622	0.6544	0.6933	0.7251	0.7310	0.8210
	S2	0.6712	0.6649	0.6923	0.7429	0.7443	0.7827

	Scenario	NN	RF	CNN	Lasso	Enet	MDeep
Sensitivity	S1	0.6620	0.6534	0.6932	0.7208	0.7290	0.8188
	S2	0.6794	0.6730	0.6932	0.7480	0.7460	0.7904
Specificity	S1	0.6692	0.6586	0.6962	0.7346	0.7350	0.8254
	S2	0.6582	0.6508	0.6958	0.7432	0.7442	0.7720
Accuracy	S1	0.6656	0.6560	0.6947	0.7277	0.7320	0.8221
	S2	0.6688	0.6619	0.6945	0.7456	0.7451	0.7812
Precision	S1	0.6709	0.6594	0.6976	0.7387	0.7406	0.8271
	S2	0.6702	0.6593	0.6951	0.7437	0.7455	0.7777
MCC	S1	0.3354	0.3139	0.3914	0.4588	0.4665	0.6468
	S2	0.3420	0.3250	0.3909	0.4935	0.4921	0.5644
F1 score	S1	0.6622	0.6544	0.6933	0.7251	0.7310	0.8210
	S2	0.6712	0.6649	0.6923	0.7429	0.7443	0.7827

Top performed method in each metric is bold.