Skip to main content

Table 5 The LogicNet in comparison with PCA-CMI, ARACNe, Genie3, Narromi, CN, and GRNTE in reconstructing the undirected yeast networks (the edge direction is not taken into account in calculating the performance). Yeast networks Y2 and Y3 are reconstructed by using 10 gene expression samples from the DREAM3 dataset. Two types of logics, i.e., the PC and the fuzzy logics, are used separately for reconstructing the GRNs and detecting the logic functions in the LogicNet algorithm. The value of c = α + β is set to 1000. The highest accuracies are indicated in boldface

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 14 10 10 11 0.56 0.50 0.58 0.53 0.06 0.57
 Fuzzy-LogicNet 14 8 12 11 0.56 0.40 0.64 0.58 0.16 0.60
 PCA-CMI-0.1 5 1 19 20 0.20 0.05 0.83 0.53 0.22 0.32
 PCA-CMI-0.05 5 2 18 20 0.20 0.10 0.71 0.51 0.14 0.31
 ARACNe 1 0 20 24 0.04 0.00 1.00 0.47 0.13 0.08
 GENIE3-FR-sqrt 5 1 19 20 0.20 0.05 0.83 0.53 0.22 0.32
 GENIE3-FR-all 3 3 17 22 0.12 0.15 0.50 0.44 −0.04 0.19
 Narromi 8 2 18 17 0.32 0.10 0.80 0.58 0.26 0.46
 CN 8 5 15 17 0.32 0.25 0.62 0.51 0.08 0.42
 GRNTE 14 9 11 11 0.56 0.45 0.61 0.56 0.11 0.58
Yeast Network Y3
 PC-LogicNet 17 7 16 5 0.77 0.30 0.71 0.73 0.47 0.74
 Fuzzy-LogicNet 14 8 15 8 0.64 0.35 0.64 0.64 0.29 0.64
 PCA-CMI-0.1 14 2 21 8 0.64 0.09 0.88 0.78 0.57 0.74
 PCA-CMI-0.05 15 6 17 7 0.68 0.26 0.71 0.71 0.42 0.70
 ARACNe 3 0 23 19 0.14 0.00 1.00 0.58 0.27 0.24
 GENIE3-FR-sqrt 3 1 22 19 0.14 0.04 0.75 0.56 0.16 0.23
 GENIE3-FR-all 3 2 21 19 0.14 0.09 0.60 0.53 0.08 0.22
 Narromi 6 5 18 16 0.27 0.22 0.55 0.53 0.06 0.36
 CN 17 7 16 5 0.77 0.30 0.71 0.73 0.47 0.74
 GRNTE 10 7 16 12 0.45 0.30 0.59 0.58 0.15 0.51