Benchmarking network algorithms for contextualizing genes of interest
Fig 2
Characterizing algorithms using average fraction of start nodes in the output to indicate tendency to return start nodes in output (A, top left) and degree to indicate tendency to return nodes with many edges (B, top right). Cross-validation performance of algorithms as indicated by the fraction of datasets for which the algorithm appeared in the top five when ranked by AUROC (C, bottom left) or Fraction recovered (D, bottom right). For the fraction recovered analysis, the top nodes were defined as the 200 top-ranked nodes for node prioritization and causal regulator algorithms or any node present in a subnetwork for subnetwork ID algorithms.