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Table 9 Performances of GA-WE and the state-of-the-art methods in the cross-species prediction

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

Dataset Species Method AUC ACC SN SP
Balanced Human Piano 0.431 0.558 0.878 0.238
piRNApredictor 0.850 0.783 0.781 0.784
Ensemble Learning 0.845 0.774 0.764 0.784
GA-WE 0.863 0.788 0.796 0.781
Drosophila Piano 0.367 0.587 0.905 0.270
piRNApredictor 0.728 0.650 0.630 0.669
Ensemble Learning 0.682 0.628 0.512 0.745
GA-WE 0.687 0.668 0.639 0.698
Imbalanced Human Piano 0.426 0.747 0.000 1.000
piRNApredictor 0.856 0.823 0.507 0.931
Ensemble Learning 0.856 0.783 0.300 0.946
GA-WE 0.868 0.811 0.425 0.942
Drosophila Piano 0.369 0.713 0.000 1.000
piRNApredictor 0.783 0.773 0.422 0.915
Ensemble Learning 0.750 0.736 0.275 0.921
GA-WE 0.746 0.774 0.370 0.936