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Table 1 Accuracies of unsupervised and supervised GRNI methods on different datasets

From: Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets

  Unsupervised method SIRENE
Datasets Method AUC AUC
DREAM3 (knockdown): genes 100, samples 100 MRNET 0.59 0.71
DREAM4 (multifactorial): genes 100, samples 100 GENIE 0.79 0.69
Ovary-normal: genes 2,450, samples 12 RN 0.55 0.62
Ovary-normal: genes 282, samples 12 RN 0.70 0.86
  1. Comparison of accuracies (AUC) of unsupervised and supervised Gene Regulatory Network Inference (GRNI) methods on different datasets. For each dataset, the best-performing unsupervised method was selected for comparison with SIRENE.
  2. AUC, area under the receiver-operating characteristic curve; DREAM, Dialogue for Reverse Engineering Assessments and Methods; GENIE, Gene Network Inference with Ensemble of Trees; MRNET, Minimum Redundancy/Maximum Relevance Networks; RN, Relevance Networks; SIRENE, Supervised Inference of Regulatory Networks.