Table 3.

Performance of PREDICT and SCMFDD on PREDICT Dataset

Methods	AUPR	AUC	SN	SP	ACC	F
PREDICT	0.1507	0.9020	0.3414	0.9929	0.9915	0.1437
SCMFDD-Che-GS	0.3141	0.9005	0.3663	0.9988	0.9974	0.3753
SCMFDD-Che-Phen	0.3153	0.9038	0.3678	0.9988	0.9974	0.3769
SCMFDD-SE-GS	0.3157	0.9082	0.3663	0.9988	0.9974	0.3753
SCMFDD-SE-Phen	0.3176	0.9109	0.3678	0.9988	0.9974	0.3769
SCMFDD-GP-GS	0.3210	0.9129	0.3720	0.9988	0.9975	0.3811
SCMFDD-GP-Phen	0.3224	0.9157	0.3714	0.9988	0.9975	0.3806
SCMFDD-GO-GS	0.3147	0.9035	0.3678	0.9988	0.9974	0.3769
SCMFDD-GO-Phen	0.3159	0.9065	0.3678	0.9988	0.9974	0.3769
SCMFDD-GW-GS	0.3249	0.9173	0.3389	0.9991	0.9977	0.3843
SCMFDD-GW-Phen	0.3284	0.9203	0.3776	0.9988	0.9975	0.3870

For drugs, Che Chemical fingerprints Similarity, SE Side Effect Similarity, GP Genes-Perlman Similarity, GO Genes- Ovaska Similarity, GW Genes-Waterman Similarity. For diseases, GS Gene Signature Similarity, Phen Phenotypic Similarity