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Table 8 Performances of GA-WE and the state-of-the-art methods on three species

From: A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs

Dataset Species Method AUC ACC SN SP
Balanced Human Piano 0.592 0.560 0.855 0.265
piRNApredictor 0.894 0.812 0.859 0.764
Ensemble Learning 0.920 0.807 0.815 0.800
GA-WE 0.932 0.839 0.858 0.820
Mouse Piano 0.445 0.5365 0.837 0.236
piRNApredictor 0.892 0.819 0.862 0.776
Ensemble Learning 0.924 0.810 0.863 0.756
GA-WE 0.937 0.838 0.826 0.850
Drosophila Piano 0.741 0.692 0.836 0.547
piRNApredictor 0.983 0.952 0.927 0.977
Ensemble Learning 0.994 0.958 0.952 0.965
GA-WE 0.995 0.959 0.949 0.966
Imbalanced Human Piano 0.449 0.747 0.000 1.000
piRNApredictor 0.905 0.847 0.548 0.949
Ensemble Learning 0.922 0.836 0.589 0.919
GA-WE 0.935 0.869 0.687 0.931
Mouse Piano 0.441 0.744 0.000 1.000
piRNApredictor 0.892 0.848 0.568 0.944
Ensemble Learning 0.928 0.849 0.586 0.940
GA-WE 0.939 0.889 0.745 0.939
Drosophila Piano 0.804 0.712 0.000 1.000
piRNApredictor 0.982 0.961 0.902 0.985
Ensemble Learning 0.995 0.965 0.920 0.984
GA-WE 0.996 0.964 0.940 0.973