Table 4.

Prediction performances of competing methods for drug set $S_{C3}$

Drug name	Fold	Feature selection	Number of	NMSPE	NMAPE
		Algorithm	Features
Nutlin-3a	1	strong-SMuRFS	149	0.7536	0.5827
		SMuRFS	563	0.7903	0.5958
		MLASSO	185	0.8245	0.6184
		MEnet	185	0.8245	0.6184
	2	strong-SMuRFS	50	0.7209	0.6495
		SMuRFS	210	0.7161	0.6586
		MLASSO	59	0.7638	0.6646
		MEnet	68	0.7552	0.609
	3	strong-SMuRFS	39	0.6795	0.5696
		SMuRFS	100	0.6778	0.5732
		MLASSO	183	0.7824	0.6309
		MEnet	184	0.7795	0.6352
	4	strong-SMuRFS	82	0.6175	0.5850
		SMuRFS	192	0.6346	0.5959
		MLASSO	30	0.6983	0.6238
		MEnet	30	0.6954	0.6209
	5	strong-SMuRFS	34	0.5714	0.5631
		SMuRFS	65	0.5788	0.5648
		MLASSO	49	0.6730	0.6258
		MEnet	49	0.6722	0.6230
PD-0332991	1	strong-SMuRFS	149	0.8508	0.7825
		SMuRFS	563	0.8763	0.7987
		MLASSO	185	0.8998	0.8039
		MEnet	185	0.8998	0.8039
	2	strong-SMuRFS	50	0.8689	0.7392
		SMuRFS	210	0.8571	0.7388
		MLASSO	59	0.8766	0.7452
		MEnet	185	0.8871	0.7507
	3	strong-SMuRFS	39	0.8394	0.7806
		SMuRFS	100	0.8529	0.7961
		MLASSO	183	0.8765	0.8022
		MEnet	185	0.8770	0.7994
	4	strong-SMuRFS	82	0.8452	0.7770
		SMuRFS	100	0.8529	0.7874
		MLASSO	30	0.7769	0.7384
		MEnet	185	0.7799	0.7410
	5	strong-SMuRFS	34	0.8794	0.7756
		SMuRFS	65	0.8853	0.7803
		MLASSO	49	0.9126	0.7942
		MEnet	185	0.9161	0.7926

Bold entries indicate best results.