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Table 2 Performances of GASA, TSNI, NCA, GAGA and GA-regular SA applied to data simulated from Eq. (2) with medium level of noise, where the averaged results of five repeats are reported

From: Inferring genetic interactions via a nonlinear model and an optimization algorithm

   # inta # pcb TPRc TNRd FPRd mFPRe
GASA AIC/no power law    0.79 0.99 0.01 0.05
  BIC/power law    0.79 0.99 0.01 0.05
GA-regular SA AIC/no power law    0.46 0.97 0.03 0.25
  BIC/power law    0.42 0.93 0.07 0.42
NCA 100% true connectivity    0.51 0.92 0.08 0.41
  50% true connectivity    0.29 0.86 0.14 0.68
TSNI Inputting prior knowledge: 26 true links 3 1 0.50 0.89 0.11 0.50
   3 2 0.50 0.89 0.11 0.50
   3 3 0.50 0.89 0.11 0.50
GA-GA AIC/no power law    0.35 0.78 0.22 0.74
  BIC/power law    0.31 0.85 0.15 0.69
  1. a '# int' denotes the number of interpolations.
  2. b '# PC' denotes the number of principal components
  3. c TPR is the ratio of the correctly predicted links to the total existing links in a simulated network. Note signs of interactions were not accounted toward TPR and other performance measure.
  4. d TNR (FPR) is the ratio of correctly predicted non-existing links (false positives) over the total true negatives.
  5. e mFPR is the ratio of incorrectly predicted links to the total predicted links.