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Table 2 Simulation results compared the number of true positives among iterative and non-iterative approaches when top 1% variables were selected

From: High-dimensional omics data analysis using a variable screening protocol with prior knowledge integration (SKI)

%a ρ b α c SISd SKIe iSISf iSKIg
0 0.3 0.061 23.32 23.12 25.22 22.53
0.5 0.3 0.342 24.83 33.20 26.13 34.43
1 0.3 0.443 23.14 34.41 26.33 38.85
0 0.6 0.044 37.35 36.34 41.11 36.17
0.5 0.6 0.392 36.47 41.67 39.67 44.83
1 0.6 0.453 37.12 45.83 40.44 49.40
  1. a%: the percentage of non-zero β’s overlapped with each other in two datasets
  2. b ρ: correlation coefficients between two neighbor variables in each cluster
  3. c α: the estimated value of α which control the weight of two ranks in geometric mean
  4. dSIS: variables were sorted by marginal correlation using only internal dataset
  5. eiSIS: iterative version of SIS
  6. fSKI: variables were sorted by weighted geometric mean of two marginal correlation based ranks using two dataset
  7. giSKI: iterative version of SKI