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Table 7 AUPR values of different algorithms under \(CV_d\) 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

iPALM-DLMF

0.549245 (0.0137)

0.392701 (0.0111)

0.398948 (0.0269)

0.399354 (0.0136)

iPALM-DLMF (without NNDSVD)

0.537198 (0.0237)

0.34756 (0.0049)

0.374837 (0.0212)

0.383282 (0.0172)

iPALM-DLMF ( \(\lambda _d\)=0)

0.135244 (0.0152)

0.072055 (0.0102)

0.053728 (0.0031)

0.016293 (0.0011)

iPALM-DLMF ( \(\lambda _t\)=0)

0.491092 (0.0268)

0.347821 (0.0059)

0.363915 (0.0201)

0.35153 (0.0038)

iPALM-DLMF ( \(\lambda _l\)=0)

0.539008 (0.0226)

0.364441 (0.0100)

0.37975 (0.0158)

0.371432 (0.0277)

PALM-DLMF

0.547462 (0.0326)

0.377908 (0.0103)

0.3656 (0.0079)

0.378476 (0.0083)

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