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Table 1 Benchmark of our NN architectures specifically designed for the WES-based case–control IBD prediction against conventional methods

From: Large sample size and nonlinear sparse models outline epistatic effects in inflammatory bowel disease

Model

ROC AUCa

Number of parameters

Best additive model

0.728 (0.00599)

1,734,301

Random forest

0.688 (0.00578)

 

NNlogreg

0.753 (0.0117)

24,379

NNbiosparse

0.758 (0.00689)

25,503

NNdense

0.743 (0.00944)

6,515,063

NNlinear

0.717 (0.0261)

25,503

  1. aPerformance given as mean (standard deviation) of test set ROC AUC from 10 different full threefold cross-validation runs with identical splits across models