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Table 2 Prediction AUCs for cross-dataset experiments

From: The application of network label propagation to rank biomarkers in genome-wide Alzheimer’s data

Dataset # SNPs Method Number of SNPs used in classifier
1 2 5 10 50 100 500 1000
TGen (Feature selection from ADRC) 64,984 (ADRC overlap, chr1-22) Chi Sq 0.6086 ± 0.0294 0.6863 ± 0.0280 0.7099 ± 0.0270 0.6958 ± 0.0253 0.6563 ± 0.0286 0.6097 ± 0.0296 0.5593 ± 0.0310 0.5563 ± 0.0308
SWRF 0.5952 ± 0.0296 0.6980 ± 0.0274 0.6994 ± 0.0272 0.7005 ± 0.0274 0.6756 ± 0.0284 0.6677 ± 0.0284 0.5635 ± 0.0306 0.5195 ± 0.0310
SLR 0.6086 ± 0.0294 0.6863 ± 0.0280 0.7164 ± 0.0269 0.7289 ± 0.0263 0.6522 ± 0.0292 0.6084 ± 0.0300 * *
LP 0.5023 ± 0.0306 0.6039 ± 0.0300 0.7023 ± 0.0272 0.7037 ± 0.0274 0.6888 ± 0.0276 0.6543 ± 0.0286 0.6114 ± 0.0298 0.5690 ± 0.0306
ADRC (Feature selection from TGen) 64,984 (TGen overlap, chr1-22) Chi Sq 0.6172 ± 0.0231 0.6385 ± 0.0229 0.7419 ± 0.0204 0.7362 ± 0.0208 0.6695 ± 0.0225 0.6479 ± 0.0227 0.5396 ± 0.0239 0.5259 ± 0.0122
SWRF 0.5397 ± 0.0239 0.5345 ± 0.0241 0.5350 ± 0.0241 0.5401 ± 0.0243 0.5042 ± 0.0243 0.5257 ± 0.0241 0.5201 ± 0.0241 0.5053 ± 0.0241
SLR 0.5397 ± 0.0214 0.7006 ± 0.0214 0.7003 ± 0.0214 0.7048 ± 0.0216 0.6048 ± 0.0233 0.5854 ± 0.0237 * *
LP 0.5397 ± 0.0239 0.6021 ± 0.0235 0.7283 ± 0.0210 0.7366 ± 0.0208 0.6853 ± 0.0220 0.6598 ± 0.0225 0.5678 ± 0.0239 0.5306 ± 0.0239
  1. The entries are the cross-dataset classification AUCs and the 95% confidence intervals. Feature selection was applied to one dataset, and the top-ranked features were used to derive and evaluate a kNN classifier on the other dataset. The SLR method is implicitly feature-selective, reducing the feature space to under 500 features for all experiments (indicated by cells containing *).