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Table 6 The LogicNet in comparison with ARACNe, Genie3, Narromi, CN, and GRNTE in reconstructing the directed yeast networks. Two Yeast networks, i.e., Y2 and Y3 with 10 genes and 25 edges (Y2)/22 edges (Y3), are reconstructed by the LogicNet by using 10 gene expression samples from the DREAM3 dataset

From: LogicNet: probabilistic continuous logics in reconstructing gene regulatory networks

Method TP FP TN FN TPR FPR PPV ACC MCC F-measure
Yeast Network Y2
 PC-LogicNet 10 20 45 15 0.40 0.31 0.33 0.61 0.09 0.36
 Fuzzy-LogicNet 8 18 47 17 0.32 0.28 0.31 0.61 0.04 0.31
 ARACNe 0 1 64 25 0.00 0.02 0.00 0.71 −0.07
 GENIE3-FR-sqrt 1 5 60 24 0.04 0.08 0.17 0.68 −0.07 0.06
 GENIE3-FR-all 1 5 60 24 0.04 0.08 0.17 0.68 −0.07 0.06
 Narromi 6 5 60 19 0.24 0.08 0.55 0.73 0.22 0.33
 CN 1 5 60 24 0.04 0.08 0.17 0.68 −0.07 0.06
 GRNTE 12 17 48 13 0.48 0.26 0.41 0.67 0.21 0.44
Yeast Network Y3
 PC-LogicNet 11 16 52 11 0.50 0.24 0.41 0.70 0.25 0.45
 Fuzzy-LogicNet 8 19 49 14 0.36 0.28 0.30 0.63 0.08 0.33
 ARACNe 1 2 66 21 0.05 0.03 0.33 0.74 0.04 0.08
 GENIE3-FR-sqrt 2 2 66 20 0.09 0.03 0.50 0.76 0.13 0.15
 GENIE3-FR-all 1 5 63 21 0.05 0.07 0.17 0.71 −0.05 0.07
 Narromi 5 7 61 17 0.23 0.10 0.42 0.73 0.16 0.29
 CN 6 11 57 16 0.27 0.16 0.35 0.70 0.12 0.31
 GRNTE 7 15 53 15 0.32 0.22 0.32 0.67 0.10 0.32