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Table 1 Performance comparison between different layer architectures on RPI488

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

Architecture Method Accuracy Sensitivity Specificity Precision MCC AUC
Sep-256-128-64 IPMiner 0.891 0.939 0.831 0.945 0.784 0.914
  SDA-RF 0.880 0.922 0.827 0.928 0.762 0.904
  SDA-FT-RF 0.881 0.916 0.831 0.926 0.762 0.909
Con-256-128-128 IPMiner 0.872 0.893 0.843 0.894 0.743 0.903
  SDA-RF 0.884 0.924 0.831 0.934 0.770 0.911
  SDA-FT-RF 0.864 0.885 0.836 0.887 0.727 0.898
Raw input RPISeq-RF 0.880 0.926 0.822 0.932 0.762 0.903
Raw input lncPro 0.870 0.900 0.827 0.910 0.740 0.901
  1. Raw input is concatenation of 3-mer frequency features of protein and 4-mer frequency features of RNA
  2. The boldface indicates this measure performance is the best among the compared methods for individual dataset