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Table 1 ROC scores of one transcriptomics and two proteomics datasets

From: A two-step site and mRNA-level model for predicting microRNA targets

Dataset   SVM PITA TScan miRan PITAT TS_C mirT2 PicTa
Linsley All 0.81 0.76 0.75 0.55 0.60 0.61 0.63 0.58
  ROC10*n 0.0129 0.0018 0.0173 0.0105 0.0035 0.0170 0.0196 0.0113
  7m+C 0.73 0.61 0.69 0.43 0.57 0.59 0.67 0.57
Selback All 0.64 0.61 0.61 0.52 0.55 0.55 0.54 0.53
  ROC10*n 0.0253 0.0042 0.0212 0.0079 0.0138 0.0213 0.0231 0.0210
  7m+C 0.71 0.61 0.69 0.42 0.60 0.63 0.60 0.58
Baek All 0.56 0.56 0.56 0.51 0.52 0.53 0.52 0.52
  ROC10*n 0.0193 0.0046 0.0157 0.0081 0.0148 0.0174 0.0086 0.0131
  7m+C 0.59 0.60 0.62 0.44 0.56 0.61 0.54 0.54
  1. Three benchmarks, All, ROC10*n, 7m+C (7mer+Conservation), were performed on one transcripomics (Linsley) and two proteomics (Baek and Selback) datasets. The ROC scores were calculated for eight algorithms, SVM, PITA, TScan (TargetScan), miRan (miRanda), PITAT (PITA Top), TS_C (TargetScan with conserved genes), mirT2 (mirTarget2) and PicTa (PicTar)