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Table 3 Performance comparison on non-structure-based NPInter2.0 and RPI13254

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

Dataset Method Accuracy Sensitivity Specificity Precision MCC AUC
NPInter2.0 IPMiner 0.952 0.946 0.959 0.945 0.904 0.995
  SDA-RF 0.937 0.940 0.935 0.941 0.876 0.975
  SDA-FT-RF 0.934 0.953 0.912 0.955 0.868 0.990
  RPISeq-RF 0.944 0.940 0.949 0.940 0.889 0.978
  lncPro 0.928 0.919 0.938 0.917 0.856 0.971
RPI13254 IPMiner 0.945 0.905 0.995 0.895 0.896 0.985
  SDA-RF 0.699 0.717 0.658 0.741 0.400 0.761
  SDA-FT-RF 0.813 0.728 0.998 0.626 0.675 0.901
  RPISeq-RF 0.739 0.766 0.688 0.790 0.480 0.817
  lncPro 0.712 0.716 0.701 0.723 0.424 0.792
  1. For RPI13254, it has 13524 positive pairs and 5172 negative pairs. Here we randomly sub-sampling positive pairs from original paper to create balanced dataset, so it actually consists of 5172 negative pairs and 5172 positive pairs
  2. The boldface indicates this measure performance is the best among the compared methods for individual dataset