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Table 3 Performance of PREDICT and SCMFDD on PREDICT Dataset

From: Predicting drug-disease associations by using similarity constrained matrix factorization

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

  1. 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