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Table 2 Performance comparison on structure-based RPI369, RPI2241 and RPI1807

From: IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction

Dataset Method Accuracy Sensitivity Specificity Precision MCC AUC
RPI2241 IPMiner 0.824 0.833 0.812 0.836 0.650 0.906
  SDA-RF 0.648 0.653 0.630 0.665 0.296 0.687
  SDA-FT-RF 0.783 0.890 0.645 0.920 0.592 0.898
  RPISeq-RF 0.646 0.652 0.630 0.663 0.293 0.690
  lncPro 0.654 0.659 0.640 0.669 0.310 0.722
RPI369 IPMiner 0.752 0.735 0.791 0.713 0.507 0.773
  SDA-RF 0.707 0.699 0.727 0.689 0.416 0.754
  SDA-FT-RF 0.693 0.664 0.784 0.602 0.396 0.728
  RPISeq-RF 0.704 0.705 0.702 0.707 0.409 0.767
  lncPro 0.704 0.708 0.696 0.713 0.409 0.740
RPI1807 IPMiner 0.986 0.982 0.993 0.978 0.972 0.998
  SDA-RF 0.972 0.970 0.981 0.962 0.944 0.995
  SDA-FT-RF 0.972 0.955 0.997 0.940 0.944 0.995
  RPISeq-RF 0.973 0.968 0.984 0.960 0.946 0.996
  lncPro 0.969 0.965 0.981 0.955 0.938 0.994
  1. The positive pairs are all from original papers. The negative pairs for RPI1807 is from original paper
  2. The boldface indicates this measure performance is the best among the compared methods for individual dataset