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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 setsMethodsAcc(%)TPR(%)TNR(%)PPV(%)MCC(%)AUC
RPI369IPMiner75.273.579.171.350.70.773
RPISeq-RF70.470.570.270.740.90.767
lncPro70.470.869.671.340.90.740
RPI-SAN74.974.178.771.750.40.778
RPI-SE88.4483.6995.8780.8577.730.924
RPI488IPMiner89.193.983.194.578.40.914
RPISeq-RF88.092.682.293.276.20.903
lncPro87.090.082.791.074.00.901
RPI-SAN89.794.383.795.279.30.920
RPI-SE89.3094.4983.4895.1579.310.904
RPI1807IPMiner98.698.299.397.897.20.998
RPISeq-RF97.396.898.496.094.60.996
lncPro96.996.598.195.593.80.994
RPI-SAN96.193.699.991.492.40.999
RPI-SE96.8696.7197.6995.8393.650.994