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Table 5 Comparison of TRL-FM with baseline RL. AUCs when RL (baseline) and TRL-FM are applied to build a classification rule model on three datasets, Petalidis (brain), KangA (IPF), and Lapointe (prostate). For TRL-FM, the FMs are the medium through which knowledge transfer occurs. “Union” is an ensemble of all FMs. The mean and the standard error of the mean (SEM) for the AUC of a dataset was obtained by 10-fold cross-validation

From: Knowledge transfer via classification rules using functional mapping for integrative modeling of gene expression data

Dataset Petalidis KangA Lapointe
  AUC (SEM) AUC (SEM) AUC (SEM)
Baseline 0.83 (0.06) 0.86 (0.07) 0.93 (0.03)
FM1 0.82 (0.07) 0.93 (0.05) 0.87 (0.04)
FM2 0.89 (0.07) 0.92 (0.05) 0.88 (0.04)
FM3 0.89 (0.07) 0.85 (0.07) 0.96 (0.02)
FM4 0.88 (0.06) 0.89 (0.07) 0.90 (0.03)
FM5 0.84 (0.08) 0.81 (0.07) 0.90 (0.03)
FM6 0.85 (0.06) 0.86 (0.07) 0.94 (0.02)
FM7 0.81 (0.07) 0.82 (0.07) 0.95 (0.03)
FM8 0.86 (0.06) 0.93 (0.05) 0.92 (0.03)
FM9 0.81 (0.07) 0.86 (0.07) 0.88 (0.04)
FM10 0.84 (0.08) 0.86 (0.07) 0.89 (0.03)
FM11 0.89 (0.07) 0.82 (0.07)  
FM12   0.86 (0.07)  
FM13   0.93 (0.05)  
FM14   0.93 (0.05)  
Union 0.91 (0.06) 0.97 (0.03) 0.97 (0.02)
  1. For each dataset, positive transfer is shown in bold font, while underlined AUCs denote negative transfer