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Table 8 AUPRC per bacterial species and mean AUPRC ± standard deviation for each model

From: Promotech: a general tool for bacterial promoter recognition

Model M. smegmatis L. phytofermentans B. amyloliquefaciens R. capsulatus Mean AUPRC
RF-HOT 0.955 0.626 0.608 0.691 0.720 ± 0.161
RF-TETRA 0.800 0.608 0.843 0.678 0.732 ± 0.108
GRU-0 0.646 0.486 0.486 0.588 0.552 ± 0.079
GRU-1 0.622 0.490 0.500 0.576 0.547 ± 0.063
LSTM-3 0.625 0.499 0.494 0.559 0.544 ± 0.061
LSTM-4 0.623 0.501 0.505 0.573 0.550 ± 0.059
MULTiPly 0.649 0.474 0.653 0.591 0.592 ± 0.083
iPro70-FMWin 0.652 0.582 0.774 0.594 0.65 ± 0.088
bTSSFinder (0.512, 0.272) (0.507, 0.944) (0, 0) (0.513, 0.250) NA
G4PromFinder (0.506, 0.938) (0.448, 0.216) (0.382, 0.339) (0.510, 0.960) NA
BProm (0.781, 0.006) (0.501, 0.560) (0.701, 0.421) (0.615, 0.011) NA
  1. AUPRC is roughly the weighted average precision across all recall levels. A perfect classifier has an AUPRC of 1, while a random classifier has an AUPRC of 0.5 in a balanced data set. These results were obtained in balanced data sets (i.e., with a 1:1 ratio of positive to negative instances). The numbers in bold indicate the model with the highest AUPRC. For BPROM, bTSSFinder, and G4PromFinder, the numbers between brackets indicate precision and recall achieved as these tools did not provide a probability associated to each instance in the data set