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Table 5 AUPR values of different algorithms under \(CV_t\) scenario

From: Graph regularized non-negative matrix factorization with \(L_{2,1}\) norm regularization terms for drug–target interactions prediction

Method

NR

GPCR

IC

E

BLM-NII [15]

0.40149 (0.0618)

0.439848 (0.0259)

0.640928 (0.0191)

0.589524 (0.0069)

WKNKN [16]

0.421919 (0.0382)

0.536317 (0.0281)

0.741412 (0.0131)

0.720789 (0.0100)

RLS-WNN [14]

0.437335 (0.0206)

0.537046 (0.0235)

0.760776 (0.0169)

0.674211 (0.0266)

GRMF [31]

0.422442 (0.0486)

0.531487 (0.0175)

0.745256 (0.0091)

0.760562 (0.0100)

WGRMF [31]

0.417925 (0.0447)

0.567606 (0.0201)

0.800896 (0.0036)

0.799641 (0.0185)

CMF [29]

0.415443 (0.0407)

0.432831 (0.0596)

0.752132 (0.0154)

0.731174 (0.0140)

SRCMF [33]

0.378573 (0.0318)

0.589037 (0.0183)

0.774355 (0.0117)

0.746004 (0.0198)

MK-TCMF [34]

0.380124 (0.0098)

0.338609 (0.0071)

0.654037 (0.0086)

0.584139 (0.005)

iPALM-DLMF

0.474567 (0.0461)

0.590447 (0.0225)

0.776349 (0.0076)

0.772684 (0.0126)

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