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Table 2 The LogicNet in comparison with PCA-CMI, ARACNe, Genie3, Narromi, CN, and GRNTE in reconstructing the undirected/directed E. coli network, using 10 gene expression samples and 100 repeats of the whole simulation study. Two types of logics, i.e., PC and fuzzy logics, are used separately for reconstructing the GRNs and logic functions in the LogicNet algorithm. Also, the value of c = α + β is set to 1000. The highest accuracies are indicated in boldface. Reported values for the TP, FP, TN, FN are the total of the corresponding values over 100 repeats of the whole simulation study

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

Method TP FP TN FN TPR FPR PPV ACC MCC F-measure
Undirected E. coli Network (the edge direction is not taken into account in calculating the performance)
 PC-LogicNet 724 157 2843 776 0.48 0.05 0.82 0.79 0.51 0.61
 Fuzzy-LogicNet 640 155 2845 860 0.43 0.05 0.81 0.77 0.46 0.56
 PCA-CMI-0.1 824 1974 1026 676 0.55 0.66 0.29 0.41 −0.11 0.38
 PCA-CMI-0.05 940 2214 786 560 0.63 0.74 0.30 0.38 −0.11 0.40
 ARACNe 160 140 2860 1340 0.11 0.05 0.53 0.67 0.11 0.18
 GENIE3-FR-sqrt 213 228 2772 1287 0.14 0.08 0.48 0.66 0.10 0.22
 GENIE3-FR-all 192 235 2765 1308 0.13 0.08 0.45 0.66 0.08 0.20
 Narromi 490 829 2171 1010 0.33 0.28 0.37 0.59 0.05 0.35
 CN 976 2297 703 524 0.65 0.77 0.30 0.37 −0.12 0.41
 GRNTE 420 750 2241 1089 0.16 0.41 0.36 0.34 − 0.28 0.22
Directed E. coli Network
 PC-LogicNet 624 588 6912 876 0.42 0.08 0.51 0.84 0.37 0.46
 Fuzzy-LogicNet 540 405 7095 960 0.36 0.05 0.57 0.85 0.37 0.44
 ARACNe 120 180 7320 1380 0.08 0.02 0.40 0.83 0.12 0.13
 GENIE3-FR-sqrt 155 445 7055 1345 0.10 0.06 0.26 0.80 0.07 0.15
 GENIE3-FR-all 156 444 7056 1344 0.10 0.06 0.26 0.80 0.07 0.15
 Narromi 275 1513 5987 1225 0.18 0.20 0.15 0.70 −0.02 0.17
 CN 616 1369 6131 884 0.41 0.18 0.31 0.75 0.21 0.35
 GRNTE 232 1210 3030 1128 0.04 0.59 0.13 0.15 −0.63 0.08