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Table 9 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

iPALM-DLMF

0.47678 (0.0461)

0.590447 (0.0225)

0.776349 (0.0076)

0.772684 (0.0126)

iPALM-DLMF (without NNDSVD)

0.445602 (0.0205)

0.532892 (0.0346)

0.764708 (0.0091)

0.745737 (0.0173)

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

0.393418 (0.0226)

0.53539 (0.0257)

0.769829 (0.0190)

0.751815 (0.0132)

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

0.101816 (0.0137)

0.049806 (0.0048)

0.072554 (0.0109)

0.018001 (0.0018)

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

0.474567 (0.0287)

0.525707 (0.0335)

0.767536 (0.0137)

0.751271 (0.0068)

PALM-DLMF

0.432608 (0.0479)

0.514862 (0.0368)

0.766604 (0.0066)

0.770734 (0.0090)

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