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Table 4 AUC values of different algorithms under \(CV_t\) scenario

From: Graph regularized non-negative matrix factorization with prior knowledge consistency constraint for drug–target interactions prediction

Method

NR

GPCR

IC

E

BLM-NII

0.795604 (0.0217)

0.856269 (0.0071)

0.930531 (0.0029)

0.917814 (0.0056)

WKNKN

0.700475 (0.0430)

0.835764 (0.0217)

0.922583 (0.0079)

0.916965 (0.0042)

RLS-WNN

0.763799 (0.0208)

0.884184 (0.0128)

0.941532 (0.0031)

0.926638 (0.0053)

GRMF

0.753382 (0.0293)

0.876011 (0.0063)

0.920496 (0.0060)

0.920224 (0.0074)

WGRMF

0.749512 (0.0384)

0.883883 (0.0083)

0.945641 (0.0024)

0.933971 (0.0161)

CMF

0.75651 (0.0520)

0.855621 (0.0164)

0.924479 (0.0051)

0.924598 (0.0161)

SRCMF

0.614843 (0.0333)

0.840992 (0.0127)

0.926765 (0.0049)

0.913015 (0.0082)

ADA-GRMFC

0.799721 (0.0154)

0.896419 (0.0245)

0.948086 (0.0038)

0.939765 (0.0070)

  1. The maximum AUC on each dataset is shown in bold. Standard deviation is shown in parentheses