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Table 3 Compared RPI-SE with other computational methods on RPI369, RPI488 and RPI1807 data sets

From: RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information

Data sets Methods Acc(%) TPR(%) TNR(%) PPV(%) MCC(%) AUC
RPI369 IPMiner 75.2 73.5 79.1 71.3 50.7 0.773
RPISeq-RF 70.4 70.5 70.2 70.7 40.9 0.767
lncPro 70.4 70.8 69.6 71.3 40.9 0.740
RPI-SAN 74.9 74.1 78.7 71.7 50.4 0.778
RPI-SE 88.44 83.69 95.87 80.85 77.73 0.924
RPI488 IPMiner 89.1 93.9 83.1 94.5 78.4 0.914
RPISeq-RF 88.0 92.6 82.2 93.2 76.2 0.903
lncPro 87.0 90.0 82.7 91.0 74.0 0.901
RPI-SAN 89.7 94.3 83.7 95.2 79.3 0.920
RPI-SE 89.30 94.49 83.48 95.15 79.31 0.904
RPI1807 IPMiner 98.6 98.2 99.3 97.8 97.2 0.998
RPISeq-RF 97.3 96.8 98.4 96.0 94.6 0.996
lncPro 96.9 96.5 98.1 95.5 93.8 0.994
RPI-SAN 96.1 93.6 99.9 91.4 92.4 0.999
RPI-SE 96.86 96.71 97.69 95.83 93.65 0.994